Patent Publication Number: US-2023153647-A1

Title: Recommendations using graph machine learning-based regression

Description:
FIELD 
     The embodiments discussed in the present disclosure are related to recommendations using graph machine learning-based regression. 
     BACKGROUND 
     Advancements in the field of knowledge representation and reasoning have led to development of numerous graph-based recommendations. One of the common types of graph is a knowledge graph that represents objects, entities, events, situations, and associated interdependencies in the form of a graph-structured data model. The knowledge graph typically includes nodes and edges connecting the nodes. Every edge represents a relationship between two connecting nodes. Many recommender systems use a knowledge graph to provide recommendations to users based on a similarity between information represented by various types of nodes, and their connections and relations. 
     The subject matter claimed in the present disclosure is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some embodiments described in the present disclosure may be practiced. 
     SUMMARY 
     According to an aspect of an embodiment, a method may include a set of operations which may include querying a graph database to extract a set of subgraphs, each of which may include information associating a respective entity of an entity group with an item. The set of operations may further include computing a label score for each subgraph of the set of subgraphs. The label score may be indicative of an importance of the item to the respective entity. The set of operations may further include generating a training dataset to include the set of subgraphs and the label score for each subgraph of the set of subgraphs. The set of operations may further include training a set of machine learning (ML) regression models on respective entity-specific subsets of the training dataset and providing an unseen subgraph associated with a first entity as an input to an ML regression model of the trained set of ML regression models. The trained ML regression model may be associated with a second entity that may be different from the first entity. The set of operations may further include generating a prediction score as an output of the ML regression model for the input and determining, from the set of subgraphs, one or more subgraphs associated with the second entity. The one or more subgraphs may be determined based on the prediction score. The set of operations may further include determining a recommendation for one or more items, based on the one or more subgraphs and controlling a user device associated with the first entity to display the recommendation. 
     The objects and advantages of the embodiments will be realized and achieved at least by the elements, features, and combinations particularly pointed out in the claims. 
     Both the foregoing general description and the following detailed description are given as examples and are explanatory and are not restrictive of the invention, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Example embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which: 
         FIG.  1    is a diagram representing an example environment related to recommendations using graph machine learning-based regression; 
         FIG.  2    is a block diagram that illustrates an exemplary system for recommendations using graph machine learning-based regression; 
         FIG.  3    is a diagram that illustrates an exemplary scenario of an extraction of a subgraph associated with an entity and an item related to the entity from a graph database including an example graph; 
         FIG.  4    is a diagram that illustrates a flowchart of an example method for extraction of a set of subgraphs from a graph database; 
         FIG.  5    is a diagram that illustrates an exemplary graph in which each entity corresponds to a researcher and each item corresponds to a research journal associated with a respective researcher; 
         FIG.  6    is a diagram that illustrates exemplary operations for extraction of a set of subgraphs from a graph database; 
         FIG.  7    is a diagram that illustrates a flowchart of an example method for determination of recommendations using graph machine learning-based regression; 
         FIG.  8    is a diagram that illustrates a block diagram of an example method for determination of recommendations using graph machine learning-based regression; 
         FIG.  9    is a diagram that illustrates a flowchart of an example method for determination of a third score associated with a first subgraph from a set of subgraphs extracted from a graph database; 
         FIG.  10    is a diagram that illustrates a flowchart of an example method for determination of a fourth score associated with a first subgraph from a set of subgraphs extracted from a graph database; 
         FIG.  11    is a diagram that illustrates a flowchart of an example method for determination of a set of adjusted scores associated with a set of subgraphs extracted from a graph database; 
         FIG.  12    is a diagram that illustrates a flowchart of an example method for determination of a third score for an item associated with a first subgraph from a set of subgraphs extracted from a graph database; and 
         FIG.  13    is a diagram that illustrates a block diagram of an example scenario for determination of recommendations associated with an entity of an unseen graph by using graph machine learning-based regression, 
     
    
    
     all according to at least one embodiment described in the present disclosure. 
     DESCRIPTION OF EMBODIMENTS 
     Some embodiments described in the present disclosure relate to methods and systems for recommendations using graph machine learning-based regression. In the present disclosure, a graph database may be queried to extract a set of subgraphs. Each of the extracted set of subgraphs may include information associating a respective entity of an entity group with an item. Further, a label score may be computed for each subgraph of the set of subgraphs. The label score may be indicative of an importance of the item to the respective entity. Thereafter, a training dataset may be generated to include the set of subgraphs and the label score for each subgraph. A set of machine learning (ML) regression models may be trained on respective entity-specific subsets of the training dataset. Thereafter, an unseen subgraph associated with a first entity may be provided as an input to an ML regression model of the trained set of ML regression models. The trained ML regression model may be associated with a second entity, which may be different from the first entity. Thereafter, a prediction score may be generated as an output of the ML regression model for the input. Further, from the set of subgraphs, one or more subgraphs associated with the second entity may be determined, based on the prediction score. A recommendation for one or more items may be determined, based on the one or more subgraphs. Further, a user device associated with the first entity may be controlled to display the recommendation. 
     According to one or more embodiments of the present disclosure, the technological field of knowledge representation and reasoning may be improved by configuring a computing system in a manner that the computing system may be able to determine recommendations using graph machine learning-based regression. The computing system may train a set of ML regression models on the respective entity-specific subsets of the training dataset including the set of subgraphs and the label score for each subgraph extracted from the graph database. On an input of an unseen subgraph associated with the first entity to the ML regression model associated with the second entity, a prediction score may be generated. The generated prediction score may be used to determine one or more subgraphs associated with the second entity from the set of subgraphs. Finally, a recommendation for one or more items may be determined, based on the one or more subgraphs. 
     The purpose of the recommendation is to suggest to user specific items, based on analysis of the data connecting the multiplicity of many users to many items of similar kind. The data connecting the users to the items are represented as a knowledge graph, where nodes in the graph may represent the entities of object, situations, or concepts, and edges in the graph may represent relationships between the entities. In one embodiment, the example in this disclosure refers to recommending university researcher (users) to academic research publications (journals), but the same recommendation task is equally applicable to different endpoint items like customers, restaurants, products, and the like. 
     In contrast to conventional systems, the disclosed system (i.e., a subgraph based learning system) may capture full subgraph linkage information of intermediate nodes, which may be lie between nodes representative of entities and nodes representative of items. The disclosed system may use such subgraph linkage information for recommendation of the one or more items associated with an input unseen graph. Further, the disclosed subgraph-based learning system may incorporate more information into the recommendation generation process than conventional node-based learning systems. For example, implicit or latent relations and patterns in the subgraph linkage information may be learned by the disclosed subgraph-based learning system. The conventional systems, on the other hand, may use a priori knowledge of the graph data or other heuristics of the graph data structure that may be insufficient, biased or, inaccurate. Further, the disclosed system may provide a better performance and may be more scalable than traditional graph-based solutions (such as typical content-based or collaborative-filtering recommendation systems), as the disclosed system may avoid inefficiencies related to global update of similarity scores into the graph database, which may be required in the traditional solutions. 
     Embodiments of the present disclosure are explained with reference to the accompanying drawings. 
       FIG.  1    is a diagram representing an example environment related to recommendations using graph machine learning-based regression, arranged in accordance with at least one embodiment described in the present disclosure. With reference to  FIG.  1   , there is shown an environment  100 . The environment  100  may include a system  102 , a graph database  104 , a user device  106 , and a communication network  114 . The system  102  and the user device  106  may be communicatively coupled to each other, via the communication network  114 . Further, the system  102  may be associated with the graph database  104 . The graph database  104  may store a knowledge graph, such as a graph  108 . The system  102  may host a set of machine learning (ML) regression models  110 . In  FIG.  1   , there is shown a user  116  who may be associated with the system  102  or the user device  106 . There is further shown a set of subgraphs  112  that may be extracted from the graph database  104  (for example, from the graph  108  stored on the graph database  104 ) and communicated to the system  102 . 
     The system  102  may include suitable logic, circuitry, interfaces, and/or code that may be configured to determine recommendations for users using graph machine learning-based regression, as described herein. 
     The system  102  may be configured to query the graph database  104  to extract the set of subgraphs  112 . Each subgraph of the extracted set of subgraphs  112  may include information associating a respective entity of an entity group with an item. The entity may correspond to one of a user, a group of users who works together to achieve a common objective, or an organization. For example, the disclosure (described herein) may be applicable for generation of research journal recommendations. For such a recommendation, an entity-item pair associated with a subgraph may be a researcher and a research journal associated with the researcher. The disclosure may be applicable to various other domains, such as, but not limited to, patent search, retail product search, social networking, and other commercial applications. For example, the disclosure may be applicable for patent prior art search between authors and patents, which may represent entity-item pairs associated with a subgraph. In case of retail product search, the disclosure may be applicable for product recommendations to customers or other users. Such users (and customers) and products can be represented through entity-item pairs associated with a subgraph. In case of social networking, the disclosure may be applicable to new friend recommendations to users. For such a recommendation, a user and a social connection of the user may correspond to an entity-item pair associated with a subgraph. Further, the disclosure may be used in commercial applications for sales lead generations. In such a case, a salesperson and a customer may be an entity-item pair associated with a subgraph. An example of a subgraph extracted from the graph database  104  is provided in  FIG.  3   . The extraction of the set of subgraphs  112  from the graph database  104  is described further, for example, in  FIGS.  4  and  6   . 
     The system  102  may be configured to compute a label score for each subgraph of the set of subgraphs  112 . The label score may be indicative of an importance of the item to the respective entity. After computing the label score, the system  102  may be configured to generate a training dataset to include the set of subgraphs  112  and the label score for each subgraph of the set of subgraphs  112 . The system  102  may be further configured to train the set of ML regression models  110  on respective entity-specific subsets of the training dataset. Each ML regression models of the set of ML regression models  110  may be a graph ML model. Each ML regression model may be trained to output a prediction score for a subgraph input to the ML regression model. The predication score may be used to recommend items associated with the input subgraph. 
     Once trained, the system  102  may be configured to provide an unseen subgraph associated with a first entity as an input to an ML regression model of the trained set of ML regression models  110 . The trained ML regression model may be associated with a second entity that may be different from the first entity. The system  102  may be further configured to generate a prediction score as an output of the ML regression model for the input. The prediction score may be indicative of an importance of items associated with the second entity for the first entity. 
     The system  102  may be configured to determine, from the set of subgraphs  112 , one or more subgraphs associated with the second entity, based on the prediction score. Thereafter, the system  102  may determine a recommendation for one or more items, based on the one or more subgraphs. The one or more items in the recommendation may be determined based on an association of the one or more items with the one or more subgraphs. The recommendation may be for items that may be relevant for the first entity. Each subgraph of the determined one or more subgraphs associated with the second entity may be associated with certain items. The prediction score associated with the unseen subgraph may be output from the ML regression model associated with the second entity. As the one or more subgraphs associated with the second entity may be determined based on the prediction score (for the unseen graph), items associated with such subgraphs may be of relevance to the first entity. Such items may therefore be recommended to the first entity (or a user associated with the first entity). The system  102  may be configured to control a user device (e.g., the user device  106 ) associated with the first entity (e.g., the user  116 ) to display the recommendation. The determination of the recommendation is described further, for example, in  FIGS.  7 ,  8 , and  13   . 
     In an embodiment, the work of subgraph extraction, label score computation, ML training, and prediction may be performed on different machines, especially since all graph-related operations can be largely memory intensive and the ML work can be GPU intensive. All such machines may be considered as part of the system  102 . In another embodiment, the system  102  may include two different machines that have a shared storage for intermediate data. 
     Examples of the system  102  may include, but are not limited to, a recruitment engine or machine, a mobile device, a desktop computer, a laptop, a computer work-station, a computing device, a mainframe machine, a server such as a cloud server, and a group of servers. In one or more embodiments, the system  102  may include a user-end terminal device and a server communicatively coupled to the user-end terminal device. The system  102  may be implemented using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some other instances, the system  102  may be implemented using a combination of hardware and software. 
     The graph database  104  may include suitable logic, interfaces, and/or code that may be configured to store the graph  108 . The graph database  104  may be derived from data off a relational or non-relational database, or a set of comma-separated values (csv) files in conventional or big-data storage. The graph database  104  may be stored or cached on a device, such as a server or the system  102 . The device storing the graph database  104  may be configured to receive a query for the set of subgraphs  112  from the system  102 . In response, the server of the graph database  104  may be configured to retrieve and provide the queried set of subgraphs  112  to the system  102  based on the received query. 
     In some embodiments, the graph database  104  may be hosted on a plurality of servers stored at same or different locations. The operations of the graph database  104  may be executed using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some other instances, the graph database  104  may be implemented using software. 
     The user device  106  may include suitable logic, circuitry, interfaces, and/or code that may be configured to execute a data mining task for creation of the graph  108 . For example, the user device  106  may include a web-client software or an electronic mail software, through which the user device  106  may receive data associated with a set of entities and a set of items (related to each entity of the set of entities). The user device  106  may include data mining software that may be used to create the graph  108  based on application of data mining operations on the received data. The user device  106  may upload the created graph  108  to the system  102 . In addition, the user device  106  may upload the created graph  108  to the graph database  104  for storage. 
     The user device  106  may be further configured to receive the recommendation for the one or more items. The user device  106  may display the received recommendation for the one or more items on a display screen of the user device  106  for the user  116 . In some embodiments, the user device  106  may receive a query from the user  116  to determine the recommendation for the one or more items. The user device  106  may further send the query to the system  102  and initiate the determination of the recommendation by the system  102 . Examples of the user device  106  may include, but are not limited to, a mobile device, a desktop computer, a laptop, a computer work-station, a computing device, a mainframe machine, a server, such as a cloud server, and a group of servers. Although in  FIG.  1   , the user device  106  is separated from the system  102 ; however, in some embodiments, the user device  106  may be integrated in the system  102 , without a deviation from the scope of the disclosure. 
     The set of ML regression models  110  may be a set of regression models, each of which may be trained to identify a relationship between inputs, such as features in a training dataset and to predict scores that indicate importance of items associated with one entity for the other entity. In an embodiment, each ML regression model of the set of ML regression models  110  may be a graph ML model. Herein, the training dataset may include the set of subgraphs  112  and the label score for each subgraph of the set of subgraphs  112 . Each of the set of ML regression models  110  may be trained on a respective entity-specific subsets of the training dataset. Further, each of the set of ML regression models  110  may be defined by topology and hyper-parameters of the respective ML regression model, for example, number of weights, cost function, input size, number of layers, and the like. The parameters of the ML model, for example, weights, may be updated so as to move towards a global minima of a cost function for the ML model. After several epochs of the training on the feature information in the training dataset, the ML model may be deployed to output a prediction score for a set of inputs (e.g., a subgraph). The prediction score for an input subgraph may be indicative of an importance of items associated with an entity that corresponds to the input subgraph. 
     The ML model may include electronic data, which may be implemented as, for example, a software component of an application executable on the system  102 . The ML model may rely on libraries, external scripts, or other logic/instructions for execution by a processor (such as, a processor  202  of  FIG.  2   ). The ML model may include code and routines configured to enable the system  102  to perform one or more operations, such as, determine a predication score associated with an input unseen subgraph associated with an entity. Additionally, or alternatively, the ML model may be implemented using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, the ML model may be implemented using a combination of hardware and software. 
     The communication network  114  may include a communication medium through which the system  102  may communicate with the user device  106 . Though not shown in  FIG.  1   , in certain embodiment, the system  102  may also communicate with the servers of the graph database  104 , via the communication network  114 . Examples of the communication network  114  may include, but are not limited to, the Internet, a cloud network, a Wireless Fidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local Area Network (LAN), and/or a Metropolitan Area Network (MAN). Various devices in the environment  100  may be configured to connect to the communication network  114 , in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, at least one of a Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, IEEE 802.11, light fidelity(Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication, wireless access point (AP), device to device communication, cellular communication protocols, and/or Bluetooth (BT) communication protocols, or a combination thereof. 
     Modifications, additions, or omissions may be made to  FIG.  1    without departing from the scope of the present disclosure. For example, the environment  100  may include more or fewer elements than those illustrated and described in the present disclosure. For instance, in some embodiments, the environment  100  may include the system  102  but not the graph database  104  and the user device  106 . In addition, in some embodiments, the functionality of each of the graph database  104  and the user device  106  may be incorporated into the system  102 , without a deviation from the scope of the disclosure. 
       FIG.  2    is a block diagram of a system for generating recommendations using graph machine learning-based regression, arranged in accordance with at least one embodiment described in the present disclosure.  FIG.  2    is explained in conjunction with elements from  FIG.  1   . With reference to  FIG.  2   , there is shown a block diagram  200  of a system  102 . The system  102  may include a processor  202 , a memory  204 , a persistent data storage  206 , an input/output (I/O) device  208 , a display screen  210 , a network interface  212 , and the set of ML regression models  110 . 
     The processor  202  may include suitable logic, circuitry, and/or interfaces that may be configured to execute program instructions associated with different operations to be executed by the system  102 . For example, some of the operations may include querying the graph database  104 , computing the label score, generating the training dataset, training the set of ML regression models  110 , providing the unseen subgraph, generating the prediction score, determining the one or more subgraphs, determining the recommendation, and controlling the user device (e.g., the user device  106 ). The processor  202  may include any suitable special-purpose or general-purpose computer, computing entity, or processing device including various computer hardware or software modules and may be configured to execute instructions stored on any applicable computer-readable storage media. For example, the processor  202  may include one or more of a microprocessor, a graphical processing unit (GPU), a microcontroller, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a Field-Programmable Gate Array (FPGA), or any other digital or analog circuitry configured to interpret and/or to execute program instructions and/or to process data. 
     Although illustrated as a single processor in  FIG.  2   , the processor  202  may include any number of processors configured to, individually or collectively, perform or direct performance of any number of operations of the system  102 , as described in the present disclosure. Additionally, one or more of the processors may be present on one or more different electronic devices, such as different servers. In some embodiments, the processor  202  may be configured to interpret and/or execute program instructions and/or process data stored in the memory  204  and/or the persistent data storage  206 . In some embodiments, the processor  202  may fetch program instructions from the persistent data storage  206  and load the program instructions in the memory  204 . After the program instructions are loaded into the memory  204 , the processor  202  may execute the program instructions. Some of the examples of the processor  202  may be a Graphics Processing Unit (GPU), a Central Processing Unit (CPU), a Reduced Instruction Set Computer (RISC) processor, an ASIC processor, a Complex Instruction Set Computer (CISC) processor, a co-processor, and/or a combination thereof. 
     The memory  204  may include suitable logic, circuitry, interfaces, and/or code that may be configured to store program instructions executable by the processor  202 . In certain embodiments, the memory  204  may be configured to store operating systems and associated application-specific information. The memory  204  may include computer-readable storage media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable storage media may include any available media that may be accessed by a general-purpose or special-purpose computer, such as the processor  202 . By way of example, and not limitation, such computer-readable storage media may include tangible or non-transitory computer-readable storage media including Random Access Memory (RAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory devices (e.g., solid state memory devices), or any other storage medium which may be used to carry or store particular program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer. Combinations of the above may also be included within the scope of computer-readable storage media. Computer-executable instructions may include, for example, instructions and data configured to cause the processor  202  to perform a certain operation or group of operations associated with the system  102 . 
     The persistent data storage  206  may include suitable logic, circuitry, interfaces, and/or code that may be configured to store program instructions executable by the processor  202 , operating systems, and/or application-specific information, such as logs and application-specific databases. The persistent data storage  206  may include computer-readable storage media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable storage media may include any available media that may be accessed by a general-purpose or a special-purpose computer, such as the processor  202 . 
     By way of example, and not limitation, such computer-readable storage media may include tangible or non-transitory computer-readable storage media including Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices (e.g., Hard-Disk Drive (HDD)), flash memory devices (e.g., Solid State Drive (SSD), Secure Digital (SD) card, other solid state memory devices), or any other storage medium which may be used to carry or store particular program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer. Combinations of the above may also be included within the scope of computer-readable storage media. Computer-executable instructions may include, for example, instructions and data configured to cause the processor  202  to perform a certain operation or group of operations associated with the system  102 . 
     In some embodiments, either of the memory  204 , the persistent data storage  206 , or combination may store the set of subgraphs  112  extracted and received from the queried graph database  104 , the computed label score, the generated training dataset, and the determined recommendation for the one or more items. Either of the memory  204 , the persistent data storage  206 , or combination may further store the set of ML regression models  110 . 
     The I/O device  208  may include suitable logic, circuitry, interfaces, and/or code that may be configured to receive a user input. For example, the I/O device  208  may receive a user input to receive the graph  108  from a website or email software. In another example, the I/O device  208  may receive a user input to generate or edit the graph  108 , and/or store the generated/edited graph  108  on the system  102  and/or the graph database  104 . The I/O device  208  may further receive a user input that may include an instruction to determine the recommendation based on an input of an unseen subgraph. The I/O device  208  may be further configured to provide an output in response to the user input. For example, the I/O device  208  may display the recommendations of the one or more items (as may be determined by the system  102 ) on the display screen  210 . The I/O device  208  may include various input and output devices, which may be configured to communicate with the processor  202  and other components, such as the network interface  212 . Examples of the input devices may include, but are not limited to, a touch screen, a keyboard, a mouse, a joystick, and/or a microphone. Examples of the output devices may include, but are not limited to, a display (e.g., the display screen  210 ) and a speaker. 
     The display screen  210  may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to display the recommendations of the one or more items based on an input unseen subgraph associated with the first entity. The display screen  210  may be configured to receive the user input from the user  116 . In such cases the display screen  210  may be a touch screen to receive the user input. The display screen  210  may be realized through several known technologies such as, but not limited to, a Liquid Crystal Display (LCD) display, a Light Emitting Diode (LED) display, a plasma display, and/or an Organic LED (OLED) display technology, and/or other display technologies. 
     The network interface  212  may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to establish a communication between the system  102 , the graph database  104 , and the user device  106 , via the communication network  114 , or directly. The network interface  212  may be implemented by use of various known technologies to support wired or wireless communication of the system  102 , via the communication network  114 . The network interface  212  may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and/or a local buffer. 
     Modifications, additions, or omissions may be made to the example system  102  without departing from the scope of the present disclosure. For example, in some embodiments, the example system  102  may include any number of other components that may not be explicitly illustrated or described for the sake of brevity. 
       FIG.  3    is a diagram that illustrates an exemplary scenario of extracting a subgraph associated with an entity and an item related to the entity from a graph database, in accordance with an embodiment of the disclosure.  FIG.  3    is described in conjunction with elements from  FIG.  1    and  FIG.  2   . With reference to  FIG.  3   , there is shown an exemplary scenario  300 . The scenario  300  includes a graph (such as the graph  108 ) stored in the graph database  104 . The graph stored in the graph database  104  may be a knowledge graph. The knowledge graph (e.g., the graph  108 ) may include a set of distinct entity nodes associated with the entity group and a set of distinct item nodes corresponding to a set of items. For example, as shown in  FIG.  3   , the graph  108  may include a first entity node  302  associated with an entity “e1”. The graph  108  may further include a first item node  304  associated with an item “i2”. 
     The knowledge graph (e.g., the graph  108 ) may further include the set of subgraphs  112 , each of which may lie between at least one entity node of the set of distinct entity nodes and at least one item node of the set of distinct item nodes. The information in each subgraph of the set of subgraphs  112  may include a set of intermediate nodes connected to an entity node and an item node through a set of edges. Each intermediate node of the set of intermediate nodes may represent an attribute that may be linked to the item. For example, as shown in  FIG.  3   , the graph  108  may include a first subgraph  308  that may lie between the first entity node  302  (associated with the entity “e1”) and the first item node  304  (associated with the item “i2”). The first subgraph  308  may include a first set of intermediate nodes including a first node  306 A, a second node  306 B, and a third node  306 C. Each of the first set of intermediate nodes may represent an attribute that may be linked to the item “i2” associated with the first item node  304 . The extraction of the set of subgraphs  112  from the graph database  104  is described further, for example, in  FIGS.  4  and  6   . 
     In an embodiment, the entity may correspond to one of a user, a group of users who works together to achieve a common objective, or an organization. In an embodiment, the respective entity of the entity group may correspond to a researcher and the item may correspond to a research journal with which the researcher may be associated. The set of intermediate nodes may include a set of key-phrase nodes and a set of topic nodes. A relevancy weight may be assigned to an edge between each key-phrase node of the set of key-phrase nodes and a corresponding topic node of the set of topic nodes. An example of a graph and a subgraph associated with researchers and research journals is described further, for example, in  FIG.  5   . 
     It should be noted that the scenario  300  shown in  FIG.  3    is presented merely as example and should not be construed to limit the scope of the disclosure. 
       FIG.  4    is a diagram that illustrates a flowchart of an example method for extraction of a set of subgraphs from a graph database, in accordance with an embodiment of the disclosure.  FIG.  4    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   , and  FIG.  3   . With reference to  FIG.  4   , there is shown a flowchart  400 . The method illustrated in the flowchart  400  may start at  402  and may be performed by any suitable system, apparatus, or device, such as by the example system  102  of  FIG.  1    or the processor  202  of  FIG.  2   . Although illustrated with discrete blocks, the steps and operations associated with one or more of the blocks of the flowchart  400  may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation. 
     At  402 , a set of distinct entity nodes may be determined in the graph database  104 . In an embodiment, the processor  202  may be configured to determine the set of distinct entity nodes in the graph database  104 . For instance, the processor  202  may query the graph database  104  to determine a set of distinct entity nodes associated with a set of distinct entities of an entity group. The set of distinct entity nodes may be determined from the graph  108  stored in the graph database  104 . The determination of the set of distinct entity nodes is described further, for example, in  FIG.  6   . 
     At  404 , a set of distinct item nodes may be determined in the graph database  104 . In an embodiment, the processor  202  may be configured to determine the set of distinct item nodes in the graph database  104 . Specifically, the processor  202  may query the graph database  104  to determine the set of distinct items nodes. Such nodes may be associated with a set of distinct items and may be identified from the graph  108  stored in the graph database  104 . The determination of the set of distinct item nodes is described further, for example, in  FIG.  6   . 
     At  406 , for each pair of an entity node (e.g., the first entity node  302 , for the entity “e1”) of the set of distinct entity nodes and an item node (e.g., the first item node  304 , for the item “i2”) of the set of distinct item nodes, a set of operations may be performed. The set of operations may include a first operation ( 406 A) and a second operation ( 406 B). In an embodiment, the processor  202  may be configured to perform the set of operations for each pair of an entity node of the set of distinct entity nodes and an item node of the set of distinct item nodes. 
     At  406 A, the first operation may be performed.  202 . The first operation may include extraction of a first subgraph of a plurality of subgraphs from the graph  108  stored in the graph database  104 . In an embodiment, information in the extracted first subgraph may include a set of intermediate nodes connected to an entity node and an item node through a set of edges. Each intermediate node of the et of intermediate nodes may represent an attribute that may be linked to the item. For example, the information may include the first set of intermediate nodes (e.g., the first node  306 A, the second node  306 B, and the third node  306 C) and a set of edges including a first edge between the first node  306 A and the third node  306 C, and a second edge between the second node  306 B and the third node  306 C. 
     At  406 B, the second operation may be performed. The second operation may include a trimming of one or more intermediate nodes from the set of intermediate nodes, based on a relevancy attribute that may be assigned to each edge of the set of edges. The trimming of the one or more intermediate nodes may executed to remove nodes associated with attributes that may not be of much relevance to the item associated with the item node. The first operation and the second operation are described further, for example, in  FIG.  6   . 
     At  408 , the set of subgraphs  112  may be extracted from the plurality of subgraphs by removing one or more duplicate subgraphs from the plurality of subgraphs. In an embodiment, the processor  202  may be configured to extract the set of subgraphs  112  from the plurality of subgraphs by removing the one or more duplicate subgraphs from the plurality of subgraphs. The removal of the one or more duplicate subgraphs may be executed to avoid a data duplicity bias that may be introduced in a training dataset (described in  FIG.  6   ) to be generated from the set of subgraphs. The extraction of the set of subgraphs  112  is described further, for example, in  FIG.  6   . Control may pass to end. 
     Although the flowchart  400  is illustrated as discrete operations, such as  402 ,  404 ,  406  (including  406 A and  406 B), and  408 . However, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the particular implementation without detracting from the essence of the disclosed embodiments. 
       FIG.  5    is a diagram that illustrates an exemplary graph in which each entity corresponds to a researcher and each item corresponds to a research journal associated with a respective researcher, in accordance with an embodiment of the disclosure.  FIG.  5    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   , FIG.  3 , and  FIG.  4   . With reference to  FIG.  5   , there is shown an exemplary graph  500 . The graph  500  may be stored in the graph database  104  and may be referred to as a knowledge graph. The graph  500  includes three entity nodes, each of which may correspond to a researcher. For example, a first entity node corresponds to a first researcher (e.g., “User 1”), a second entity node corresponds to a second researcher (e.g., “User 2”), and a third entity node corresponds to a third researcher (e.g., “User 3”). The graph  500  further includes three item nodes, each of which may correspond to a research journal associated with one or more researchers. As an example, the graph  500  includes a first item node corresponding to a first research journal (e.g., “Journal 1”), a second item node corresponding to a second research journal (e.g., “Journal 2”), and a third item node corresponding to a third research journal (e.g., “Journal 3”). 
     The graph  500  further includes a set of intermediate nodes that may include a set of key-phrase nodes and a set of topic nodes. Each topic node of the set of topic nodes may be associated with at least one entity node (representing a researcher) and at least one item node (representing a research journal associated with the researcher). Each topic node may represent a topic associated with one or more research journals. For example, the topic associated with a research journal may be include information such as, but not limited to, a topic or subtopic of research, an application domain, a problem being solved in an application domain, or a solution to a problem in an application domain. Each key-phrase node of the set of key-phrase nodes may be associated with one or more topic nodes and may represent a key-phrase (i.e. a phrase or set of words) that may be used to index research papers associated with a certain research journal. The key-phrase nodes may also be used to search for research papers associated with a certain research journal. In an embodiment, a relevancy weight may be assigned to an edge between each key-phrase node of the set of key-phrase nodes and a corresponding topic node of the set of topic nodes. The relevancy weight assigned to the edge may indicate a degree of relevancy of a key-phrase associated with the key-phrase node with respect to a topic associated with the corresponding topic node. 
     For example, a set of intermediate nodes may lie between the first entity node (representing the first researcher, e.g., “User 1”) and the second item node (representing the second research journal, e.g., “Journal 2”). The set of intermediate nodes may include a first topic node (representing a first topic, e.g., “Topic  1 ”), a first key-phrase node (representing a first key-phrase, e.g., “Keyphrase  1 ”), and a second key-phrase node (representing a second key-phrase, e.g., “Keyphrase  2 ”). Also, a first relevancy weight (e.g., “Relevance  1 ”) may be assigned to an edge between the first topic node and the first key-phrase node. Further, a second relevancy weight (e.g., “Relevance  2 ”) may be assigned to an edge between the first topic node and the second key-phrase node. Herein, the first topic, the first key-phrase, and the second key-phrase may be attributes of the second research journal. 
     In an embodiment, the processor  202  may extract a subgraph  502  from the graph  500 . The extracted subgraph  502  may include a set of intermediate nodes (such as the first topic node, the first key-phrase node, and the second key-phrase node). The extracted subgraph  502  may be referred as a contribution graph of the first researcher (e.g., “User 1”) for the first research journal (e.g., “Journal 1”). The extraction of a subgraph is described further, for example, in  FIG.  6   . 
     It should be noted that the graph  500  shown in  FIG.  5    is presented merely as example and should not be construed as limiting the scope of the disclosure. 
       FIG.  6    is a diagram that illustrates exemplary operations for extraction of a set of subgraphs from a graph database, in accordance with an embodiment of the disclosure.  FIG.  6    is explained in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   , and  FIG.  5   . With reference to  FIG.  6   , there is shown a block diagram  600  that illustrates exemplary operations of an execution pipeline of operations from  602  to  606  for extraction of the set of subgraphs  112 . Each of the set of subgraphs  112  may be associated with a certain entity (e.g., a researcher) from the graph database  104 . The exemplary operations may be executed by any computing system, for example, by the system  102  of  FIG.  1    or by the processor  202  of  FIG.  2   . 
     At  602 , a query and extraction operation may be executed. In the query and extraction operation, the processor  202  may query the graph database  104  storing a knowledge graph (such as a graph  608 ) to extract a plurality of subgraphs, each of which may be associated with a certain entity (such as a researcher). Each subgraph of the set of subgraphs  112  may include information associating a respective entity (e.g., a researcher) of an entity group with an item (such as a research journal). In the graph  608  of  FIG.  6   , “u1”, “u2”, and “u3” may represent entity nodes for researchers; “j1”, “j2”, and “j3” may represent item nodes for research journals; “t” may represent a topic node; and “k” may represent a key-phrase node. 
     For example, based on the query of the graph database  104 , the processor  202  may determine a matrix  610 , which may be associated with information included in the plurality of subgraphs. The determined matrix  610  may be referred to as a contribution graph matrix. Each element of the matrix  610  may include information associated with a subgraph between an entity node (e.g., a node representing a researcher, “μi”) and an item node (e.g., a node representing a research journal, “ji”) in the graph  608 . As shown, an element “u1, j2” may include information of a subgraph  612 A (e.g., “g1”) between the researcher “u1” and the research journal “j2”. Similarly, an element “u2, j1” may include information of a subgraph  612 B (e.g., “g2”) between the researcher “u2” and the research journal “j1”, while an element “u2, j3” may include information of a subgraph  612 C (e.g., “g3”) between the researcher “u2” and the research journal “j3”. Further, as shown, elements “u1, j1” and “u2, j2” may be empty or may include a “Not Applicable (n/a)” entry, which may indicate that there may be no subgraph between the researcher “u1” and the research journal “j1”, or between the researcher “u2” and the research journal “j2”. 
     At  604 , a trim operation may be executed. In an embodiment, the processor  202  may be configured to execute the trim operation on one or more intermediate nodes of each extracted subgraph. The trim operation may be executed based on a relevancy attribute assigned to each edge of a set of edges between adjacent intermediate nodes of the graph  608 . For example, the trim operation may be executed on the subgraph  612 C (e.g., “g3”). Based on a relevancy attribute assigned to an edge between a first key-phrase node and a first topic node in the subgraph  612 C, the processor  202  may trim the first key-phrase node from the subgraph  612 C. The trimming may be performed based on a determination that the relevancy attribute is below a certain threshold (e.g., 30%). Based on the execution of the trimming operation on the subgraph  612 C, a subgraph  614  may be determined. The subgraph  614  may be referred to as a contribution graph associated with the researcher “u2” for the research journal “j3”. The processor  202  may update the information associated with each subgraph in the matrix  610  based on the execution of the trimming operation on the respective subgraph. 
     At  606 , a pruning operation may be executed. In an embodiment, the processor  202  may be configured to execute the pruning operation on each contribution graph (determined based on the trim operation at  604 ). For example, information of a set of contribution graphs, which may be associated with the researcher “u2” may be obtained based on a column  610 A of the matrix  610 . The processor  202  may delete duplicate contribution graphs based on the information associated with the set of contribution subgraphs. The duplicity may be determined based at least on a subgraph structure and relevancy weights. In case of duplicate contribution graphs, the processor  202  may store, in the memory  204 , one copy of the contribution graph along with an identity of each research journal for which the duplicate contribution graphs have been identified. Based on the execution of the pruning operation of the set of contribution graphs associated with the researcher “u2”, a pruned set of contribution graphs  616  may be determined. 
     In an embodiment, the pruned set of contribution graphs  616  may be stored in the memory  204  as a list along with identities of each research journal corresponding to respective contribution graphs of the pruned set of contribution graphs  616 . For example, the pruned set of contribution graphs  616  may include a contribution graph “cg2” for the research journal “j1”, a contribution graph “cg3” for the research journal “j3”, . . . and a contribution graph for the research journals “jx” and “jy”. 
     It should be noted that the block diagram  600  shown in  FIG.  6    is presented merely as example and should not be construed as limiting the scope of the disclosure. 
       FIG.  7    is a diagram that illustrates a flowchart of an example method for determination of recommendations using graph machine learning-based regression, in accordance with an embodiment of the disclosure.  FIG.  7    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   , and  FIG.  6   . With reference to  FIG.  7   , there is shown a flowchart  700 . The method illustrated in the flowchart  700  may start at  702  and may be performed by any suitable system, apparatus, or device, such as by the example system  102  of  FIG.  1    or the processor  202  of  FIG.  2   . Although illustrated with discrete blocks, the steps and operations associated with one or more of the blocks of the flowchart  700  may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation. 
     At  702 , the graph database  104  may be queried to extract the set of subgraphs  112 . Each of the set of subgraphs  112  may include information that may associate a respective entity of the entity group with a corresponding item. In an embodiment, the processor  202  may be configured to query the graph database  104  to extract the set of subgraphs  112  from a knowledge graph (such as the graph  108 ) stored in the graph database  104 . Details related to the query and the extraction of the set of subgraphs  112  are provided further, for example, in  FIGS.  4  and  6   . 
     At  704 , a label score may be computed for each subgraph of the set of subgraphs  112 . The label score may be indicative of an importance of an item to a respective entity. In an embodiment, the processor  202  may be configured to compute the label score for each subgraph of the set of subgraphs  112 . In an embodiment, the label score associated with a first subgraph of the set of subgraphs  112  may be computed based on at least one of a first score, a second score, a third score, or a fourth score. The first subgraph may be associated with a third entity, which may be different from the first entity and the second entity. 
     The first score may include a sum of nodes and edges of the first subgraph. For example, with reference to  FIG.  5   , for the subgraph  502 , the first score may be equal to 5 (i.e., 3 nodes+2 edges). The first score may be referred as a graph size. The second score may include an edge-to-node ratio for the first subgraph. For example, for the subgraph  502 , the second score may be equal to ‘⅔’ as the subgraph graph  308  may include 2 edges and 3 nodes. The second score may also be referred as a graph density. Further, the third score may include a relevancy-based key-phrase score for an item associated with the first subgraph. The third score may be referred to as a total k-score. The fourth score may include a relative key-phrase score for the item associated with the first subgraph. The fourth score may be referred to as a top k-score percentage. The determination of the third score and the fourth score are described further, for example, in  FIG.  9    and  FIG.  10   , respectively. 
     In an embodiment, the first score and the third score may be absolute scores. Whereas the second score and the fourth score may be relative scores. While the first score and the second score may be associated with a structure of the first subgraph, the third score and the fourth score may be associated with key-phrases associated with the first subgraph. For example, in cases where the entity is a researcher and the item is a research journal, a large value of the first score may indicate that a research journal has a large number of topics and key-phrases. The large value of the first score may indicate that a contribution graph of the researcher and the corresponding research journal may be large. Further, a large value of the second score may indicate that topics associated with a research journal have a high degree of similarity. The large value of the second score may also indicate that the contribution graph corresponding to the research journal is dense. Further, a high value of the third score may indicate a high count of common key-phrases that may be of a high relevance. The high value of the third score may also indicate that a certain research journal substantially matches a researcher&#39;s interest. Further, a high value of the fourth score may indicate a high percentage of common key-phrases. The high value of the fourth score may also indicate that a certain research journal is substantially focused on a corresponding researcher&#39;s interest. 
     In an embodiment, the computation of the label score may include normalizing each of the first score, the second score, the third score, and the fourth score. The normalization of a score may include division of the score by a normalization parameter, such that the normalized value of the score lies between a predefined range, such as between 0 to 1. For example, the processor  202  may determine a maximum score from among the first score, the second score, the third score, and the fourth score. Thereafter, the processor  202  may assign the maximum score of the four scores to the normalization parameter and may divide each of the four scores by the normalization parameter to normalize the four scores. The computation of the label score may further include summing each of the normalized first score, the normalized second score, the normalized third score, and the normalized fourth score. 
     At  706 , a training dataset may be generated to include the set of subgraphs  112  and the label score for each subgraph. In an embodiment, the processor  202  may be configured to generate the training dataset. The generated training dataset may include each of the set of subgraphs  112  and a label score corresponding to the respective subgraph of the set of subgraphs  112 . 
     At  708 , the set of ML regression models  110  may be trained on respective entity-specific subsets of the training dataset. In an embodiment, the processor  202  may be configured to train the set of ML regression models  110  on respective entity-specific subsets of the training dataset. For example, a particular ML regression model of the set of ML regression models  110  may be associated with a particular entity of the entity group. From the training dataset, the processor  202  may determine entity-specific subsets that may be associated with (or may be specific to) the particular entity. Thereafter, the processor  202  may train the particular ML regression model based on the determined entity-specific subsets associated with the particular entity. Herein, the entity-specific subsets may include one or more subgraphs associated with the particular entity and a label score corresponding to each of the one or more subgraphs. Training of an ML regression model is well known in the art, and hence, details related to the training of the set of ML regression models  110  are omitted for the sake of brevity. Details related to the set of ML regression models  110  are provided further, for example, in  FIG.  1   . 
     At  710 , an unseen subgraph associated with a first entity may be provided as an input to a trained ML regression model of the trained set of ML regression models. The trained ML regression model may be associated with a second entity different from the first entity. In an embodiment, the processor  202  may be configured to provide the unseen subgraph associated with the first entity as an input to the trained ML regression model associated with the second entity. The unseen subgraph may be a subgraph that may a new instance of a subgraph associated with the first entity and an item. The unseen subgraph that may not be a predetermined subgraph. 
     At  712 , a prediction score may be generated as output of the trained ML regression model for the input. In an embodiment, the processor  202  may be configured to generate the prediction score. The processor  202  may apply the trained ML regression model associated with the second entity on the unseen subgraph associated with the first entity to generate the prediction score as the output. The prediction score may be indicative of an importance of items associated with the second entity for the first entity. 
     At  714 , from the set of subgraphs  112 , one or more subgraphs associated with the second entity may be determined. In an embodiment, the processor  202  may be configured to determine the one or more subgraphs from the set of subgraphs  112 , based on the prediction score. The one or more subgraphs determined from the set of subgraphs  112  may be associated with the second entity. Further, the one or more subgraphs may be associated with one or more items related to the second entity such that the one or more items may be of importance to the first entity. The determination of the one or more subgraphs is described further, for example, in  FIGS.  12  and  13   . 
     At  716 , a recommendation for one or more items may be determined, based on the one or more subgraphs. In an embodiment, the processor  202  may be configured to determine the recommendation for the one or more items, based on the one or more subgraphs. The one or more items in the recommendation may be determined based on an association of the one or more items with the one or more subgraphs. For example, a sub graph “SG 1 ” of the one or more subgraphs may be associated with an item “I 1 ”. In such a case, the item “I 1 ” may be recommended as one of the one or more items. The determination of the recommendation for the one or more items is described further, for example, in  FIG.  13   . 
     At  718 , a user device (e.g., the user device  106 ) associated with the first entity (e.g., the user  116 ) may be controlled to display the recommendation. In an embodiment, the processor  202  may be configured to control the user device associated with the first entity to display the recommendation. For example, the processor  202  may transmit the recommendation to the user device  106  and control the user device  106  to display the transmitted recommendation. Control may pass to end. 
     Although the flowchart  700  is illustrated as discrete operations, such as  702 ,  704 ,  706 ,  708 ,  710 ,  712 ,  714 ,  716 , and  718 . However, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the particular implementation without detracting from the essence of the disclosed embodiments. 
       FIG.  8    is a diagram that illustrates a block diagram of an example method for determination of recommendations using graph machine learning-based regression, in accordance with an embodiment of the disclosure.  FIG.  8    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   ,  FIG.  6   , and  FIG.  7   . With reference to  FIG.  8   , there is shown a block diagram  800 . The block diagram  800  may include the graph database  104  and extracted sets of subgraphs  802 . The extracted sets of subgraphs  802  may include a first set of subgraphs  802 A, . . . and an Nth set of subgraphs  802 N. The block diagram  800  may further include a first set of label scores  804 A, . . . and an Nth set of label scores  804 N. Further, the block diagram  800  may include a first ML regression model  806 A, . . . and an Nth ML regression model  806 N, a first predictive model  808 A, . . . and an Nth predictive model  808 N; a first set of adjusted scores  810 A, . . . and an Nth set of adjusted scores  810 N; an unseen subgraph  812 ; and a first set of prediction scores  814 A, . . . and an Nth set of prediction scores  814 N. The block diagram  800  may further include a score ranking and recommendation operation  816 , which upon execution by the processor  202  may determine a ranking and recommendation output  818 . 
     The number of regression models, predictive models, adjusted scores, and prediction scores shown in  FIG.  8    are presented merely as examples and such a number should not be construed as limiting the disclosure. 
     In an embodiment, the processor  202  may be configured to query the graph database  104  and extract sets of subgraphs (such as the extracted sets of subgraphs  802 ). The first set of subgraphs  802 A of the extracted sets of subgraphs  802  may be associated with an entity E 1  (for example, a researcher “u1”). Similarly, the Nth set of subgraphs  802 N of the extracted sets of subgraphs  802  may be associated with an entity E N  (for example, a researcher “u N ”). The processor  202  may be configured to determine each label score of the first set of label scores  804 A for a respective subgraph of the first set of subgraphs  802 A. Similarly, the Nth set of label scores  804 N may be determined for a respective subgraph of the Nth set of subgraphs  802 N. The determination of a label score is described further, for example, in  FIG.  7   ,  FIG.  9   , and  FIG.  10   . 
     For training the first ML regression model  806 A, the processor  202  may be configured to determine a training dataset. The training dataset may include the first set of subgraphs  802 A and the first set of label scores  804 A, each of which may correspond to a respective subgraph of the first set of subgraphs  802 A. Similarly, the processor  202  may be configured to determine a training dataset for the Nth ML regression model  806 N based on the Nth set of subgraphs  802 N and the Nth set of label scores  804 N. Herein, the first ML regression model  806 A may be associated with the entity E 1  and the Nth ML regression model  806 N may be associated with the entity E N . Based on the training of each ML regression model, the processor  202  may be configured to determine a corresponding predictive model. For example, the processor  202  may determine the first predictive model  808 A based on the training of the first ML regression model  806 A and may determine the Nth predictive model  808 N based on the training of the Nth ML regression model  806 N. In an embodiment, the first predictive model  808 A may correspond to the trained first ML regression model  806 A, while the Nth predictive model  808 N may correspond to the trained Nth ML regression model  806 N. 
     In an embodiment, the processor  202  may be configured to determine a set of adjusted scores associated with each set of subgraphs of the extracted sets of subgraphs  802 . The set of adjusted score associated with a set of subgraphs may be determined based on an application of a trained ML model (i.e., a predictive model), (which may be specific to an entity associated with the set of subgraphs) on at least one subset of the set of subgraphs. For example, the processor  202  may apply the first predictive model  808 A on at least one subset of the first set of subgraphs  802 A to determine the first set of adjusted scores  810 A. The at least one subset of the first set of subgraphs  802 A may be different from one or more subgraphs of the first set of subgraphs  802 A that may be used for the training of the first ML regression model  806 A to determine the first predictive model  808 A. Further, the first set of adjusted scores  810 A may correspond to cross-validation scores for the first predictive model  808 A (i.e., the trained first ML regression model  806 A). Similarly, the processor  202  may apply the Nth predictive model  808 N on at least one subset of the Nth set of subgraphs  802 N to determine the Nth set of adjusted scores  810 N. The determination of the set of adjusted scores associated with the set of subgraphs is described further, for example, in  FIG.  12   . 
     In an embodiment, the processor  202  may receive the unseen subgraph  812  associated with the first entity. The unseen subgraph  812  may be retrieved from the extracted sets of subgraphs  802  or may be received from another source, for example, a web source (such as a website) that collects a user input from the user  116 . For example, the website may be research journal website or an e-commerce website. The processor  202  may provide the received unseen subgraph  812  as an input to each predictive model (such as the first predictive model  808 A, . . . and the Nth predictive model  808 N). Herein, each predictive model may correspond to a respective trained ML regression model of the set of trained ML regression models. For example, the first predictive model  808 A may correspond to the trained first ML regression model  806 A, . . . and the Nth predictive model  808 N may correspond to the trained Nth ML regression model  806 N. 
     Based on application of a predictive model on the received unseen subgraph  812 , the processor  202  may determine a set of prediction scores as an output for the input to the predictive model. For example, the processor  202  may determine an output that may include first set of prediction scores  814 A based on application of the first predictive model  808 A on the received unseen subgraph  812 . Similarly, the processor  202  may determine an output of the Nth set of prediction scores  814 N based on an application of the Nth predictive model  808 N on the received unseen subgraph  812 . A prediction score corresponding to a certain entity may be indicative of an importance of items associated with the particular entity for the first entity. For example, the first set of prediction scores  814 A corresponding to the entity “E 1 ” may be indicative of an importance of items associated with the entity “E 1 ” for the first entity. 
     In an embodiment, the processor  202  may be configured to execute a score ranking and recommendation operation  816 . Based on the execution of the score ranking and recommendation operation  816 , a ranking and recommendation output  818  may be determined. As part of the score ranking and recommendation operation  816 , the processor  202  may create a ranked list of adjusted scores (i.e., a ranking output from the ranking and recommendation output  818 ) from each set of adjusted scores (such as, the first set of adjusted scores  810 A, . . . and the Nth set of adjusted scores  810 N). Further, the processor  202  may index each prediction score in the first set of prediction scores  814 A, . . . and the Nth set of prediction scores  814 N within a corresponding ranked list of adjusted scores. The processor  202  may compare each indexed prediction score with each adjusted score in the corresponding ranked list of adjusted scores to determine one or more subgraphs. In other words, the index of each prediction score may be compared with the index of each respective adjusted score. For example, the index of each prediction score of the first set of prediction scores  814 A may be compared with the index of each respective adjusted prediction score of the first set of adjusted scores  810 A. Thereafter, the processor  202  may determine one or more items associated with the determined one or more subgraphs and may recommend the determined one or more items (i.e., a recommendation output from the ranking and recommendation output  818 ). For example, the processor  202  may determine items nodes that may be linked to intermediate nodes of the determined one or more subgraphs. Then, the processor  202  may recommend an item corresponding to each of the determined item nodes as the one or more items. The determination of the one or more subgraphs and the recommendation of the one or more items is described further, for example, in  FIGS.  12  and  13   . 
     It should be noted that the block diagram  800  shown in  FIG.  8    is presented merely as example and should not be construed to limit the scope of the disclosure. 
       FIG.  9    is a diagram that illustrates a flowchart of an example method for determination of a third score associated with a first subgraph from a set of subgraphs extracted from a graph database, in accordance with an embodiment of the disclosure.  FIG.  9    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   ,  FIG.  6   ,  FIG.  7   , and  FIG.  8   . With reference to  FIG.  9   , there is shown a flowchart  900 . The method illustrated in the flowchart  900  may start at  902  and may be performed by any suitable system, apparatus, or device, such as by the example system  102  of  FIG.  1    or the processor  202  of  FIG.  2   . Although illustrated with discrete blocks, the steps and operations associated with one or more of the blocks of the flowchart  900  may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation. 
     At  902 , a number of occurrences of a first key-phrase node may be determined. The first key-phrase node may be present in a set of key-phrase nodes of one or more first subgraphs of the set of subgraphs  112 . Herein, the one or more first subgraphs may be associated with a third entity, which may be different from the first entity and the second entity. For example, the first key-phrase node may represent a key-phrase “Distributed Computing” in a set of 21 key-phrase nodes. The set of 21 key-phrase nodes may include 7 instances of the first key-phrase node (i.e., the key-phrase “Distributed Computing”). In such case, the number of occurrences of the first key-phrase node may be determined as 7. 
     In an embodiment, from the set of subgraphs  112  extracted from the graph database  104 , the processor  202  may be configured to determine the one or more first subgraphs associated with the third entity. Thereafter, the processor  202  may determine the number of occurrences of the first key-phrase node in the set of key-phrase nodes. By way of example, and not limitation, in case all contribution graphs of a researcher associated with different research journals, for a certain key-phrase node, the processor  202  may determine a number of times the key-phrase node occurs among all the contribution graphs associated with the researcher. Though the determination of the third score is described with respect to the third entity, the third score may be determined similarly on a per entity basis, based on the operations  902  to  910 , as described herein. 
     At  904 , a first k-score for the first key-phrase node may be determined, based on a first ratio of the number of occurrences of the first key-phrase to a total number of the set of key-phrase nodes. In an embodiment, the processor  202  may be configured to determine the first k-score. For example, in case of the contribution graphs of the particular researcher for different research journals, the processor  202  may determine the first k-score for a key-phrase node as the first ratio. The first ratio may be a ratio of the count of a number of times the key-phrase node occurs among all the contribution graphs to a total number of key-phrase nodes in all the contribution graphs for the researcher. As an example, a certain key-phrase node (for example, “Distributed Computing”) may occur seven times in three contribution graphs of a researcher and there may be a total of 21 key-phrase node in the three contribution graphs. In such a case, the first k-score for the key-phrase node may be determined as 0.33 (i.e., 7/21). 
     In an embodiment, the processor  202  may store the determined first k-score in a k-score mapping table (which may be denoted by “KMT”), which may be stored in the memory  204 . The first k-score of a certain key-phrase node may be obtained using a retrieval operation from the KMT (for example, KMT[“Key-phrase”], where “Key-phrase” may correspond to the key-phrase string associated with the particular key-phrase node). 
     At  906 , a first weight may be assigned to the first k-score to determine a first weighted k-score for the first key-phrase node, based on a relevancy associated with the first key-phrase node. In an embodiment, the processor  202  may be configured assign the first weight to the first k-score to determine the first weighted k-score for the first key-phrase node. The processor  202  may be configured to determine one or more relevancy weights associated with at least one edge that may connect the first key-phrase node with at least one other node of the one or more first subgraphs. For the one or more first subgraphs, the processor  202  may assign the first weight to the first k-score to determine the first weighted k-score for the first key-phrase node, based on the determined one or more relevancy weights and a predefined magnification factor. For each key-phrase node in the set of key-phrase nodes, the processor  202  may be configured to determine a weighted k-score. Based on execution of operations from  902  to  906 , a set of weighted k-scores may be determined for the set of key-phrase nodes. 
     At  908 , the set of weighted k-scores, including the first weighted k-score, may be normalized for the set of key-phrase nodes. In an embodiment, the processor  202  may be configured to normalize the set of weighted k-scores. To normalize the set of weighted k-scores, the processor  202  may convert each of the set of weighted k-scores to a real number between 0 and 1 by division of each of the set of weighted k-scores by a normalization parameter. For example, the processor  202  may determine a highest k-score value from the set of weighted k-scores and may assign the determined highest k-score value to the normalization parameter. Thereafter, the processor  202  may divide each of the set of weighted k-score by the normalization parameter to obtain a normalized set of weighted k-scores. 
     At  910 , the normalized set of weighted k-scores may be summed to determine the third score for the item associated with the first subgraph. In an embodiment, the processor  202  may sum the normalized set of weighted k-scores to determine the third score associated with the first subgraph. Thus, the third score may correspond to a total k-score associated with the first subgraph. Similarly, based on the operations  902  to  910 , the third score may be determined for each item associated with the other subgraphs of the set of subgraphs  112 . Control may pass to end. 
     Although the flowchart  900  is illustrated as discrete operations, such as,  902 ,  904 ,  906 ,  908 , and  910 . However, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the particular implementation without detracting from the essence of the disclosed embodiments. 
       FIG.  10    is a diagram that illustrates a flowchart of an example method for determination of a fourth score associated with a first subgraph from a set of subgraphs extracted from a graph database, in accordance with an embodiment of the disclosure.  FIG.  10    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   ,  FIG.  6   ,  FIG.  7   ,  FIG.  8   , and  FIG.  9   . With reference to  FIG.  10   , there is shown a flowchart  1000 . The method illustrated in the flowchart  1000  may start at  1002  and may be performed by any suitable system, apparatus, or device, such as by the example system  102  of  FIG.  1    or the processor  202  of  FIG.  2   . Although illustrated with discrete blocks, the steps and operations associated with one or more of the blocks of the flowchart  1000  may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation. 
     At  1002 , a set of k-scores for the set of key-phrase nodes of the one or more first subgraphs of the set of subgraphs  112  may be sorted to select a set of top k-scores. Herein, the one or more first subgraphs may be associated with the third entity. In an embodiment, the processor  202  may be configured to sort the set of k-scores to select the set of top k-scores. The determination of the set of k-scores for the set of key-phrase nodes is described further, for example, in  FIG.  9   . For example, the set of k-scores for the set of key-phrase nodes may be sorted to select a set of top 10% k-scores of the set of k-scores. Though the determination of the fourth score is described with respect to the third entity, the fourth score may be determined similarly on a per entity basis, based on the operations  1002  to  1006 , as described herein. 
     At  1004 , a sum of the set of top k-scores and a sum of the set of k-scores may be determined. In an embodiment, the processor  202  may be configured to determine the sum of the set of top k-scores and the sum of the set of k-scores for the set of key-phrase nodes of the one or more first subgraphs. For example, a sum of the set of top 10% k-scores and a sum of all of the k-scores may be determined. 
     At  1006 , a second ratio of the sum of top k-scores to the sum of the set of k-scores may be determined to determine the fourth score associated with the first subgraph. In an embodiment, the processor  202  may be configured to determine the second ratio of the sum of top k-scores to the sum of the set of k-scores so as to determine the fourth score associated with the first subgraph. For example, the fourth score may be determined as a ratio of the sum of top 10% k-scores to the sum of all the k-scores from the set of k-scores. Control may pass to end. 
     Although the flowchart  1000  is illustrated as discrete operations, such as  1002 ,  1004 , and  1006 . However, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the particular implementation without detracting from the essence of the disclosed embodiments. 
       FIG.  11    is a diagram that illustrates a flowchart of an example method for determination of a set of adjusted scores associated with a set of subgraphs extracted from a graph database, in accordance with an embodiment of the disclosure.  FIG.  11    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   ,  FIG.  6   ,  FIG.  7   ,  FIG.  8   ,  FIG.  9   , and  FIG.  10   . With reference to  FIG.  11   , there is shown a flowchart  1100 . The method illustrated in the flowchart  1100  may start at  1102  and may be performed by any suitable system, apparatus, or device, such as by the example system  102  of  FIG.  1    or the processor  202  of  FIG.  2   . Although illustrated with discrete blocks, the steps and operations associated with one or more of the blocks of the flowchart  1100  may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation. 
     At  1102 , the training dataset may be partitioned into entity-specific subsets of the training dataset. In an embodiment, the processor  202  may be configured to partition the training dataset into the entity-specific subsets of the training dataset. For example, the training dataset may be partitioned into a first subset associated with an entity “E 1 ”, a second subset associated with an entity “E 2 ”, . . . and an Nth subset associated with an entity “E N ”. The first subset may include a first set of subgraphs associated with the entity “E 1 ” and a first set of label scores associated with the first set of subgraphs. Further, the second subset may include a second set of subgraphs associated with the entity “E 2 ” and a second set of label scores associated with the second set of subgraphs. Similarly, the Nth subset may include an Nth set of subgraphs associated with the entity “E N ” and an Nth set of label scores associated with the Nth set of subgraphs. 
     At  1104 , each training ML regression model of the set of ML regression models  110  may be cross-validated on at least one subset of the entity-specific subsets, not used in training of a respective ML regression model. In an embodiment, the processor  202  may be configured to cross-validate each training ML regression model of the set of ML regression models  110 . 
     For example, with reference to  FIG.  8   , the first ML regression model  806 A may be associated with an entity “E 1 ”. The partitioned entity-specific subset associated with the entity “E 1 ” may include the first set of subgraphs  802 A and the first set of label scores  804 A. The first ML regression model  806 A may also be associated with the entity “E 1 ”. A first subset of subgraphs of the first set of subgraphs  802 A and a respective first subset of label scores of the first set of label scores  804 A may be used for the training of the first ML regression model  806 A. The processor  202  may determine at least one subset of the entity-specific subsets that are not used in the training of the first ML regression model  806 A. For example, such a subset may be associated with another entity, such as, the entity “E N ”. 
     For example, the determined at least one subset may include a second subset of subgraphs of the Nth set of subgraphs  802 N and a respective second subset of label scores of the Nth set of label score  804 N. The processor  202  may cross-validate the first ML regression model  806 A based on the determined subsets of the training dataset. Such subsets may be remaining partitions of the training dataset that are not used in the training of the first ML regression model  806 A. Thus, for the cross-validation, the second subset of subgraphs and a respective second subset of label scores may be used to infer against the first ML regression model  806 A trained based on the first subset of subgraphs and the respective first subset of label scores. 
     At  1106 , a set of adjusted scores associated with the set of subgraphs  112  may be determined, based on the cross-validation. In an embodiment, the processor  202  may be configured to determine the set of adjusted scores associated with the set of subgraphs  112 , based on the cross-validation. For example, with reference to  FIG.  8   , the processor  202  may feed the first set of subgraphs  802 A to the first predictive model  808 A (i.e., the trained first ML regression model  806 A) to determine a set of initial scores associated with the first set of subgraphs  802 A. The trained first ML regression model  806 A may be cross-validated, as described at  1104 . Based on the cross-validation, a trained ML regression model (e.g., the first predictive model  808 A) may produce an adjusted score for each of the first set of subgraphs  802 A. Control may pass to end. 
     Although the flowchart  1100  is illustrated as discrete operations, such as  1102 ,  1104 , and  1106 . However, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the particular implementation without detracting from the essence of the disclosed embodiments. 
       FIG.  12    is a diagram that illustrates a flowchart of an example method for determination of a recommendation of one or more items corresponding to an unseen subgraph associated with a first entity, in accordance with an embodiment of the disclosure.  FIG.  12    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   ,  FIG.  6   ,  FIG.  7   ,  FIG.  8   ,  FIG.  9   ,  FIG.  10   , and  FIG.  11   . With reference to  FIG.  12   , there is shown a flowchart  1200 . The method illustrated in the flowchart  1200  may start at  1202  and may be performed by any suitable system, apparatus, or device, such as by the example system  102  of  FIG.  1    or the processor  202  of  FIG.  2   . Although illustrated with discrete blocks, the steps and operations associated with one or more of the blocks of the flowchart  1200  may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation. 
     At  1202 , a first subset of adjusted scores of the set of adjusted scores associated with the second entity may be sorted into a list. In an embodiment, the processor  202  may be configured to sort the first subset of adjusted scores into the list. As an example, the first subset of adjusted scores (with reference to  FIG.  8   ) may include scores (e.g., the Nth set of adjusted scores  810 N) that may be associated with a second entity-specific set of subgraphs (e.g., the Nth set of subgraphs  802 N associated with a second entity, e.g., the entity “E N ”). In an embodiment, the set of adjusted scores associated with the second entity may be sorted in an ascending order of values. 
     At  1204 , the list including the first subset of adjusted scores may be updated by indexing the prediction score for the unseen subgraph (e.g., the unseen subgraph  812 ) in the list. In an embodiment, the processor  202  may be configured to update the list of the first subset of adjusted scores by indexing the prediction score for the unseen subgraph  812  in the list. For example, an index of a value of the prediction score for the unseen subgraph  812  may be determined for the list of the first subset of adjusted scores. Any known indexing technique may be used to index the value of the prediction score in the list. For example, the list may be implemented as an array or a linked list data structure and the index may be implemented as a pointer or reference variable. The sorted list of the first subset of adjusted scores may be traversed in a loop to index the prediction score of the unseen subgraph. Thereafter, the list of the first subset of adjusted scores may be updated based on the indexed value of the prediction score in the list. Once the sorted list of the first subset of adjusted scores may be traversed, indexed, and updated, the operation  1206  may be executed. 
     At  1206 , the prediction score may be compared with each adjusted score of the first subset of adjusted scores. Herein, the one or more subgraphs may be determined, based on the comparison. In an embodiment, the processor  202  may be configured to compare the prediction score (e.g., the first set of prediction scores  814 A based on the first prediction model  808 A) for the unseen graph (e.g., the unseen subgraph  812 ) with each adjusted score (e.g., the first set of adjusted scores  810 A) of the first subset. For example, based on the indexing of the prediction score, the processor  202  may determine an index associated with the prediction score in the list. As the list is already sorted, each position in the sorted list may correspond to a certain index value. Thus, the index associated with the prediction score may correspond to an index of an adjusted score, in case a position of the adjusted score in the sorted list corresponds to the index value of the prediction score. In some cases, a value of such an adjusted score may be same as the prediction score. In certain cases, a difference between such adjusted score and the prediction score may be within a defined range. Based on the comparison of the prediction score with each adjusted score of the first subset of adjusted scores, the processor  202  may determine one or more subgraphs. The processor  204  may determine one or more items, that may be associated with the determined one or more subgraphs, as a recommendation corresponding to the unseen subgraph (e.g., the unseen subgraph  812 ). The determination of the one or more subgraphs is described further, for example, in  FIG.  13   . Control may pass to end. 
     Although the flowchart  1200  is illustrated as discrete operations, such as  1202 ,  1204 , and  1206 . However, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the particular implementation without detracting from the essence of the disclosed embodiments. 
       FIG.  13    is a diagram that illustrates a block diagram of an example scenario for determination of recommendations associated with an entity of an unseen graph by using graph machine learning-based regression, in accordance with an embodiment of the disclosure.  FIG.  13    is described in conjunction with elements from  FIG.  1   ,  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   ,  FIG.  6   ,  FIG.  7   ,  FIG.  8   ,  FIG.  9   ,  FIG.  10   ,  FIG.  11   , and  FIG.  12   . With reference to  FIG.  13   , there is shown an example scenario  1300 . The scenario  1300  may include an unseen subgraph  1302  associated with an item  1304 , a first predictive model  1306 A, a second predictive model  1306 B, a first sorted list  1308 A associated with a first subset of adjusted scores, a second sorted list  1308 B associated with a second subset of adjusted scores, a prediction score_1  1310 A, a prediction score_2  1310 B. The scenario  1300  may further include one or more recommended items, including an item  1312 A, an item  1312 B, and an item  1312 C. 
     The processor  202  may receive the unseen subgraph  1302 . In an example, the unseen subgraph  1302  may be a contribution graph associated with a researcher “u0” and a research journal  10 ″. The processor  202  may feed the received unseen subgraph  1302  to each of the first predictive model  1306 A and the second predictive model  1306 B. The first predictive model  1306 A may correspond to a trained first ML regression model (e.g., the trained first ML regression model  806 A) and the second predictive model  1306 B may correspond to a trained second ML regression model (e.g., the trained Nth ML regression model  806 N). 
     The processor  202  may determine the first sorted list  1308 A of adjusted scores corresponding to a first subset of subgraphs (e.g., the first set of subgraphs  802 A) and may determine the second sorted list  1308 B of adjusted scores corresponding to a second subset of subgraphs (e.g., the Nth set of subgraphs  802 N). The determination of the adjusted scores is described further, for example, in  FIG.  11   . 
     In an example, the first subset of subgraphs may be associated with a researcher “u1” and the second subset of subgraphs may be associated with a researcher “u2”. As shown in  FIG.  13   , the first sorted list  1308 A may include a contribution graph “cg1” associated with a research journal “j1”, a contribution graph “cg2” associated with a research journal “j2”, a contribution graph “cg3” associated with research journals “j3” and “j4”, and a contribution graph “cgm” associated with research journals “jx” and “jy”. Further, the second sorted list  1308 B may include a contribution graph “cg4” associated with a research journal “j4”, a contribution graph “cg5” associated with research journals “j1” and “j4”, a contribution graph “cg6” associated with a research journal “j6”, and a contribution graph “cgn” associated with a research journal “jz”. 
     The processor  202  may feed the unseen subgraph  1302  to each of the first predictive model  1306 A and the second predictive model  1306 B to determine the prediction score_1  1310 A and the prediction score_2  1310 B, respectively. The processor  202  may index the prediction score_1  1310 A in the first sorted list  1308 A and may index the prediction score_2  1310 B in the second sorted list  1308 B, as described further, for example, in  FIG.  12   . In an example, the index of the prediction score_1  1310 A in the first sorted list  1308 A and the index of the prediction score_2  1310 B may be three (3). The processor  202  may determine the contribution graphs at the third position in each of the first sorted list  1308 A and the second sorted list  1308 B as the one or more subgraphs. For example, as shown, the contribution graphs “cg3” and “cg6” from the first sorted list  1308 A and the second sorted list  1308 B, respectively, may be determined as the one or more subgraphs. The processor  202  may then determine the one or more recommended items including the item  1312 A (i.e., the research journal “j3”), the item  1312 B (i.e., the research journal “j4”), and the item  1312 A (i.e., the research journal “j6”), based on the determined one or more subgraphs “cg3” and “cg6”. The items  1312 A and  1312 B may be associated with the subgraph “cg3” and the item  1312 C may be associated with the subgraph “cg6”. 
     It should be noted that the scenario  1300  of  FIG.  13    is presented merely as an example and should not be construed to limit the scope of the disclosure. 
     Though the disclosure is described using an example of a researcher and a research journal as an entity and an item, respectively, the scope of the disclosure may not be so limited. The disclosure may be applicable to various other application domains and implementations without departure from the scope of the disclosure. In an embodiment, the disclosure may be applicable to various other domains, such as, but not limited to, patent search, retail product search, social networking, and other commercial applications. For example, the disclosure may be applicable for patent prior art search between authors and patents with assignees, citations, field, and related art as linkage information. In case of retail product search, the disclosure may be applicable for product recommendation between customers and products, with reviews, search terms, price, and purchase history as linkage information. In case of social networking, the disclosure may be applicable to friend recommendation between users and other users, with interests, locale, and friends as linkage information. Further, the disclosure may be used in commercial applications for sales lead generations between salespersons and customers, with purchase history, other businesses, and products as linkage information. 
     The disclosed system  102  may train the set of ML regression models  110  on the respective entity-specific subsets of the training dataset including the set of subgraphs  112  and the label score for each subgraph extracted from the graph database  104 . On an input of an unseen subgraph associated with the first entity to the ML regression model associated with the second entity, a prediction score may be generated. The generated prediction score may be used to determine one or more subgraphs associated with the second entity, from the set of subgraphs. Finally, a recommendation for one or more items may be determined, based on the one or more subgraphs. In contrast to conventional systems, the disclosed system  102  (i.e., a subgraph based learning system) may capture full subgraph linkage information of intermediate nodes between nodes representative of entities and nodes representative of items, and use such subgraph linkage information for recommendation of the one or more items associated with the input unseen graph. Further, the disclosed system  102  (i.e., the subgraph based learning system) may incorporate more information into the recommendation generation process than conventional node-based learning systems. For example, unforeseen relations and patterns in the subgraph linkage information may be learned based on the disclosed subgraph-based learning system. The conventional systems, on the other hand, may use a priori knowledge of the graph data or other heuristics that may be insufficient, biased or, inaccurate. Further, the disclosed system  102  may provide better performance and may be more scalable than traditional graph-based solutions (such as, typical content-based or collaborative-filtering recommendation systems), as the disclosed system  102  may avoid inefficiencies related to global update of similarity scores into the graph database  104 , which may be required in the traditional solutions. 
     Various embodiments of the disclosure may provide one or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system (such as, the example system  102 ) to perform operations. The operations may include querying a graph database to extract a set of subgraphs, each of which may include information associating a respective entity of an entity group with an item. The operations may further include computing a label score for each subgraph of the set of subgraphs. The label score may be indicative of an importance of the item to the respective entity. The operations may further include generating a training dataset to include the set of subgraphs and the label score for each subgraph of the set of subgraphs. The operations may further include training a set of machine learning (ML) regression models on respective entity-specific subsets of the training dataset. The operations may further include providing an unseen subgraph associated with a first entity as an input to an ML regression model of the trained set of ML regression models. The trained ML regression model may be associated with a second entity different from the first entity. The operations may further include generating a prediction score as an output of the ML regression model for the input. The operations may further include determining, from the set of subgraphs, one or more subgraphs associated with the second entity, based on the prediction score. The operations may further include determining a recommendation for one or more items, based on the one or more subgraphs. The operations may further include controlling a user device associated with the first entity to display the recommendation. 
     As used in the present disclosure, the terms “module” or “component” may refer to specific hardware implementations configured to perform the actions of the module or component and/or software objects or software routines that may be stored on and/or executed by general purpose hardware (e.g., computer-readable media, processing devices, etc.) of the computing system. In some embodiments, the different components, modules, engines, and services described in the present disclosure may be implemented as objects or processes that execute on the computing system (e.g., as separate threads). While some of the system and methods described in the present disclosure are generally described as being implemented in software (stored on and/or executed by general purpose hardware), specific hardware implementations or a combination of software and specific hardware implementations are also possible and contemplated. In this description, a “computing entity” may be any computing system as previously defined in the present disclosure, or any module or combination of modulates running on a computing system. 
     Terms used in the present disclosure and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including, but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes, but is not limited to,” etc.). 
     Additionally, if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. 
     In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” is used, in general such a construction is intended to include A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together, etc. 
     Further, any disjunctive word or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” should be understood to include the possibilities of “A” or “B” or “A and B.” 
     All examples and conditional language recited in the present disclosure are intended for pedagogical objects to aid the reader in understanding the present disclosure and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Although embodiments of the present disclosure have been described in detail, various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the present disclosure.