Patent Publication Number: US-2023161779-A1

Title: Multi-phase training of machine learning models for search results ranking

Description:
CROSS-REFERENCE 
     The present application claims priority to Russian Patent Application No. 2021133942, entitled “Multi-Phase Training of Machine Learning Models for Search Results Ranking,” filed on Nov. 22, 2021, the entirety of which is incorporated herein by reference. 
     FIELD OF TECHNOLOGY 
     The present technology relates to machine learning methods, and more specifically, to methods and systems for training and using transformer-based machine learning models for ranking search results. 
     BACKGROUND 
     Web search is an important problem, with billions of user queries processed daily. Current web search systems typically rank search results according to their relevance to the search query, as well as other criteria. To determine the relevance of search results to a query often involves the use of machine learning algorithms that have been trained using multiple hand-crafted features to estimate various measures of relevance. This relevance determination can be seen as, at least in part, as a language comprehension problem, since the relevance of a document to a search query will have at least some relation to a semantic understanding of both the query and of the search results, even in instances in which the query and results share no common words, or in which the results are images, music, or other non-text results. 
     Recent developments in neural natural language processing include use of “transformer” machine learning models, as described in Vaswani et al., “Attention Is All You Need,”  Advances in neural information processing systems,  pages 5998-6008, 2017. A transformer is a deep learning model (i.e. an artificial neural network or other machine learning model having multiple layers) that uses an “attention” mechanism to assign greater significance to some portions of the input than to others. In natural language processing, this attention mechanism is used to provide context to the words in the input, so the same word in different contexts may have different meanings. Transformers are also capable of processing numerous words or natural language tokens in parallel, permitting use of parallelism in training. 
     Transformers have served as the basis for other advances in natural language processing, including pretrained systems, which may be pretrained using a large dataset, and then “refined” for use in specific applications. Examples of such systems include BERT (Bidirectional Encoder Representations from Transformers), as described in Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,”  Proceedings of NAACL - HLT  2019, pages 4171-4186, 2019, and GPT (Generative Pre-trained Transformer), as described in Radford et al., “Improving Language Understanding by Generative Pre-Training,” 2018. 
     While transformers have had substantial success in natural language processing tasks, there may be some practical difficulties in using them for search ranking. For example, many large search relevance datasets include non-text data, such as information on which links have been clicked by users, which may be useful in training a ranking model. 
     SUMMARY 
     Certain non-limiting embodiments of the present technology are directed to methods and systems for training a transformer-based learning model to determine relevance parameters of search results provided by an online search platform (such as a search engine, as an example) to a given user. For example, in at least some non-limiting embodiments of the present technology, such relevance parameters may be represented by likelihood values of user interaction (such as a click or a long click) of the given user with the search results; and the transformer-based learning model may thus be trained based on specifically organized training data. 
     More specifically, developers of the present technology have appreciated that quality of ranking the search results can be improved if the transformer-based learning model is trained in two phases. In a first phase, which is also referred to herein as “a pre-training phase”, the training data is organized in a first training set of data including at least a subset of past search results and respective past search queries, however, not including any indications of whether the given user has ever interacted therewith. Thus, in the first phase of training, based on the first training set of data, the transformer-based learning model is trained to predict if the given user has interacted with each of the past search results. 
     In a second phase of training, the training data is organized in a second training set of data including only past search results with which the user has interacted and their respective past search queries. The so generated second training set of data is further used for training the transformer-based learning model to predict if the user will interact with a given in-use search result provided thereto in response to submitting a respective in-use search query. 
     Thus, during the first phase of training, the present methods and systems are directed to providing the transformer-based learning model with more tokens, on which the learning model is trained to generate the prediction, which results in determining preliminary weights for layers of the transformer-based learning model. These weights can further be finetuned during the second phase of training when the transformer-based learning model is trained based only on those past search results that include indications of positive past user interactions therewith. 
     By doing so, the methods and systems described herein allow for training the transformer-based learning model to rank the search results in a more efficient fashion using limited amount of training data. In some non-limiting embodiments of the present technology, the quality of prediction of relevancy of a search result for a specific user is improved, i.e. resulting in an improved personalized ranking. 
     In accordance with a first broad aspect of the present technology, there is provided a computer-implemented method for training a machine-learning algorithm (MLA) to rank in-use digital documents at an online search platform. The method is executable by a processor. The method comprises: receiving, by the processor, training data associated with a given user, the training data including (i) a plurality of past queries having been submitted by the given user to the online search platform; (ii) respective sets of past digital documents generated, by the online search platform, in response to submitting thereto each one of the plurality of past queries, and a given past digital document including a respective past user interaction parameter indicative of whether the given user has interacted with the given past digital document. During a first training phase, the method comprises: organizing, by the processor, the training data in a first set of training digital objects, a given training digital object of the first set of training digital objects including: (i) a respective past query from the plurality of past queries; and (ii) a predetermined number of past digital documents responsive to the respective past query; and training, by the processor, based on the first set of training digital objects, the MLA for determining, for the given training digital object of the first set of training digital objects, if the given user has interacted with each one of the predetermined number of past digital documents. Further, during a second training phase, following the first training phase, the method comprises: organizing, by the processor, the training data in a second set of training digital objects, a given training digital object of the second set of training digital including: (i) the respective past query from the plurality of past queries; and (ii) a number of past digital documents responsive to the respective training with which the given user has interacted; and training, by the processor, based on the second set of training digital objects, the MLA to determine, for a given in-use digital document, a likelihood parameter of the given user interacting with the given in-use digital document. 
     In some implementations of the method, the past digital documents associated with the given training digital objects of the first set of training digital objects have been randomly selected from a respective set of digital documents responsive to the respective past query. 
     In some implementations of the method, the respective past user interaction parameter associated with the given past digital document has been determined based on past click data of the given user. 
     In some implementations of the method, the click data includes data of at least one click of the given user on the given past digital document made in response to submitting the respective past query to the online search platform. 
     In some implementations of the method, the method further comprises: receiving, by the processor, an in-use query; retrieving, by the processor, a set of in-use digital documents responsive to the in-use query; applying, by the processor, the MLA to each one of the set of in-use digital documents to generate respective likelihood parameters of the given user interacting therewith; and using, by the processor, the respective likelihood parameters for ranking each one of the set of in-use digital documents. 
     In some implementations of the method, the using the respective likelihood parameters comprises feeding the respective likelihood parameters as an input to an other MLA, the other MLA having been configured to rank the set of in-use digital documents based at least on the respective likelihood values of the given user interacting therewith. 
     In some implementations of the method, the other MLA is an ensemble of CatBoost decision trees. 
     In some implementations of the method, the number of past digital documents responsive to the respective past query with which the given user has interacted are all the past digital documents in a respective set of digital documents responsive to the respective past query that the user has interacted with. 
     In some implementations of the method, a first total number of members in the first set of training digital objects and a second total number of members in the second set of training digital objects are the same. 
     In some implementations of the method, a first total number of members in the first set of training digital objects and a second total number of members in the second set of training digital objects are pre-determined. 
     In some implementations of the method, the MLA is a Transformer-based MLA. 
     In accordance with a second broad aspect of the present technology, there is provided a system for training a machine-learning algorithm (MLA) to rank in-use digital documents at an online search platform. The system comprises a processor and non-transitory computer readable medium storing instructions. The processor, upon executing the instructions, is configured to: receive training data associated with a given user, the training data including (i) a plurality of past queries having been submitted by the given user to the online search platform; (ii) respective sets of past digital documents generated, by the online search platform, in response to submitting thereto each one of the plurality of past queries, and a given past digital document including a respective past user interaction parameter indicative of whether the given user has interacted with the given past digital document. During a first training phase, the processor is configured to: organize the training data in a first set of training digital objects, a given training digital object of the first set of training digital objects including: (i) a respective past query from the plurality of past queries; and (ii) a predetermined number of past digital documents responsive to the respective past query; and train, based on the first set of training digital objects, the MLA for determining, for the given training digital object of the first set of training digital objects, if the given user has interacted with each one of the predetermined number of past digital documents. Further, during a second training phase, following the first training phase, the processor is configured to: organize the training data in a second set of training digital objects, a given training digital object of the second set of training digital including: (i) the respective past query from the plurality of past queries; and (ii) a number of past digital documents responsive to the respective training with which the given user has interacted; and train, based on the second set of training digital objects, the MLA to determine, for a given in-use digital document, a likelihood parameter of the given user interacting with the given in-use digital document. 
     In some implementations of the system, the processor is configured to select the past digital documents associated with the given training digital objects of the first set of training digital objects from a respective set of digital documents responsive to the respective past query randomly. 
     In some implementations of the system, the processor is further configured to determine the respective past user interaction parameter associated with the given past digital document based on past click data of the given user. 
     In some implementations of the system, the click data includes data of at least one click of the given user on the given past digital document made in response to submitting the respective past query to the online search platform. 
     In some implementations of the system, the processor is further configured to: receive an in-use query; retrieve a set of in-use digital documents responsive to the in-use query; apply the MLA to each one of the set of in-use digital documents to generate respective likelihood parameters of the given user interacting therewith; and use the respective likelihood parameters for ranking each one of the set of in-use digital documents. 
     In some implementations of the system, to use the respective likelihood parameters, the processor is further configured to feed the respective likelihood parameters as an input to an other MLA, the other MLA having been configured to rank the set of in-use digital documents based at least on the respective likelihood values of the given user interacting therewith. 
     In some implementations of the system, the other MLA is an ensemble of CatBoost decision trees. 
     In some implementations of the system, the number of past digital documents responsive to the respective past query with which the given user has interacted are all the past digital documents in a respective set of digital documents responsive to the respective past query that the user has interacted with. 
     In some implementations of the system, a first total number of members in the first set of training digital objects and a second total number of members in the second set of training digital objects are the same. 
     In some implementations of the system, a first total number of members in the first set of training digital objects and a second total number of members in the second set of training digital objects are pre-determined. 
     In some implementations of the system, the MLA is a Transformer-based MLA. 
     In the context of the present specification, a “server” is a computer program that is running on appropriate hardware and is capable of receiving requests (e.g., from client devices) over a network, and carrying out those requests, or causing those requests to be carried out. The hardware may be one physical computer or one physical computer system, but neither is required to be the case with respect to the present technology. In the present context, the use of the expression a “server” is not intended to mean that every task (e.g., received instructions or requests) or any particular task will have been received, carried out, or caused to be carried out, by the same server (i.e., the same software and/or hardware); it is intended to mean that any number of software elements or hardware devices may be involved in receiving/sending, carrying out or causing to be carried out any task or request, or the consequences of any task or request; and all of this software and hardware may be one server or multiple servers, both of which are included within the expression “at least one server”. 
     In the context of the present specification, “client device” is any computer hardware that is capable of running software appropriate to the relevant task at hand. Thus, some (non-limiting) examples of client devices include personal computers (desktops, laptops, netbooks, etc.), smartphones, and tablets, as well as network equipment such as routers, switches, and gateways. It should be noted that a device acting as a client device in the present context is not precluded from acting as a server to other client devices. The use of the expression “a client device” does not preclude multiple client devices being used in receiving/sending, carrying out or causing to be carried out any task or request, or the consequences of any task or request, or steps of any method described herein. 
     In the context of the present specification, a “database” is any structured collection of data, irrespective of its particular structure, the database management software, or the computer hardware on which the data is stored, implemented or otherwise rendered available for use. A database may reside on the same hardware as the process that stores or makes use of the information stored in the database or it may reside on separate hardware, such as a dedicated server or plurality of servers. 
     In the context of the present specification, the expression “information” includes information of any nature or kind whatsoever capable of being stored in a database. Thus, information includes, but is not limited to audiovisual works (images, movies, sound records, presentations, etc.), data (location data, numerical data, etc.), text (opinions, comments, questions, messages, etc.), documents, spreadsheets, lists of words, etc. 
     In the context of the present specification, the expression “component” is meant to include software (appropriate to a particular hardware context) that is both necessary and sufficient to achieve the specific function(s) being referenced. 
     In the context of the present specification, the expression “computer usable information storage medium” is intended to include media of any nature and kind whatsoever, including RAM, ROM, disks (CD-ROMs, DVDs, floppy disks, hard drivers, etc.), USB keys, solid state-drives, tape drives, etc. 
     In the context of the present specification, the words “first”, “second”, “third”, etc. have been used as adjectives only for the purpose of allowing for distinction between the nouns that they modify from one another, and not for the purpose of describing any particular relationship between those nouns. Thus, for example, it should be understood that, the use of the terms “first server” and “third server” is not intended to imply any particular order, type, chronology, hierarchy or ranking (for example) of/between the server, nor is their use (by itself) intended imply that any “second server” must necessarily exist in any given situation. Further, as is discussed herein in other contexts, reference to a “first” element and a “second” element does not preclude the two elements from being the same actual real-world element. Thus, for example, in some instances, a “first” server and a “second” server may be the same software and/or hardware, in other cases they may be different software and/or hardware. 
     Implementations of the present technology each have at least one of the above-mentioned object and/or aspects, but do not necessarily have all of them. It should be understood that some aspects of the present technology that have resulted from attempting to attain the above-mentioned object may not satisfy this object and/or may satisfy other objects not specifically recited herein. 
     Additional and/or alternative features, aspects and advantages of implementations of the present technology will become apparent from the following description, the accompanying drawings and the appended claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects and advantages of the present technology will become better understood with regard to the following description, appended claims and accompanying drawings where: 
         FIG.  1    depicts a schematic diagram of an example computer system for implementing certain non-limiting embodiments of systems and/or methods of the present technology; 
         FIG.  2    depicts a networked computing environment suitable for training a machine learning model to determine likelihood values of a given user interacting with digital documents generated by an online search platform, in accordance with certain non-limiting embodiments of the present technology; 
         FIG.  3    depicts a block diagram of a machine learning model architecture run by a server present in the networked computing environment of  FIG.  2   , in accordance with certain non-limiting embodiments of the present technology; 
         FIG.  4    depicts a schematic diagram of a process for organizing, by the server present in the networked computing environment of  FIG.  2   , training data for training the machine learning model of  FIG.  3   , during a first phase of the training of the machine learning model, in accordance with certain non-limiting embodiments of the present technology; 
         FIG.  5    depicts a schematic diagram of a process for organizing, by the server present in the networked computing environment of  FIG.  2   , training data for training the machine learning model of  FIG.  3    during a second phase of the training the machine learning model in accordance with certain non-limiting embodiments of the present technology; and 
         FIG.  6    depicts a flowchart diagram of a method of training the machine learning model of  FIG.  3    to determine the likelihood values of the given user interacting with the digital documents, in accordance with certain non-limiting embodiments of the present technology. 
     
    
    
     DETAILED DESCRIPTION 
     The examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the present technology and not to limit its scope to such specifically recited examples and conditions. It will be appreciated that those skilled in the art may devise various arrangements which, although not explicitly described or shown herein, nonetheless embody the principles of the present technology and are included within its spirit and scope. 
     Furthermore, as an aid to understanding, the following description may describe relatively simplified implementations of the present technology. As persons skilled in the art would understand, various implementations of the present technology may be of a greater complexity. 
     In some cases, what are believed to be helpful examples of modifications to the present technology may also be set forth. This is done merely as an aid to understanding, and, again, not to define the scope or set forth the bounds of the present technology. These modifications are not an exhaustive list, and a person skilled in the art may make other modifications while nonetheless remaining within the scope of the present technology. Further, where no examples of modifications have been set forth, it should not be interpreted that no modifications are possible and/or that what is described is the sole manner of implementing that element of the present technology. 
     Moreover, all statements herein reciting principles, aspects, and implementations of the present technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether they are currently known or developed in the future. Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the present technology. Similarly, it will be appreciated that any flowcharts, flow diagrams, state transition diagrams, pseudo-code, and the like represent various processes which may be substantially represented in computer-readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. 
     The functions of the various elements shown in the figures, including any functional block labeled as a “processor” or a “graphics processing unit,” may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, and/or by a plurality of individual processors, some of which may be shared. In some embodiments of the present technology, the processor may be a general-purpose processor, such as a central processing unit (CPU) or a processor dedicated to a specific purpose, such as a graphics processing unit (GPU). Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read-only memory (ROM) for storing software, random-access memory (RAM), and/or non-volatile storage. Other hardware, conventional and/or custom, may also be included. 
     Software modules, or simply modules which are implied to be software, may be represented herein as any combination of flowchart elements or other elements indicating performance of process steps and/or textual description. Such modules may be executed by hardware that is expressly or implicitly shown. 
     With these fundamentals in place, we will now consider some non-limiting examples to illustrate various implementations of aspects of the present technology. 
     Computer System 
     With reference to  FIG.  1   , there is depicted a computer system  100  suitable for use with some implementations of the present technology. The computer system  100  comprises various hardware components including one or more single or multi-core processors collectively represented by a processor  110 , a graphics processing unit (GPU)  111 , a solid-state drive  120 , a random-access memory  130 , a display interface  140 , and an input/output interface  150 . 
     Communication between the various components of the computer system  100  may be enabled by one or more internal and/or external buses  160  (e.g. a PCI bus, universal serial bus, IEEE 1394 “Firewire” bus, SCSI bus, Serial-ATA bus, etc.), to which the various hardware components are electronically coupled. 
     The input/output interface  150  may be coupled to a touchscreen  190  and/or to the one or more internal and/or external buses  160 . The touchscreen  190  may be part of the display. In some non-limiting embodiments of the present technology, the touchscreen  190  is the display. The touchscreen  190  may equally be referred to as a screen  190 . In the embodiments illustrated in  FIG.  1   , the touchscreen  190  comprises touch hardware  194  (e.g., pressure-sensitive cells embedded in a layer of a display allowing detection of a physical interaction between a user and the display) and a touch input/output controller  192  allowing communication with the display interface  140  and/or the one or more internal and/or external buses  160 . In some embodiments, the input/output interface  150  may be connected to a keyboard (not shown), a mouse (not shown) or a trackpad (not shown) allowing the user to interact with the computer system  100  in addition to or instead of the touchscreen  190 . 
     It is noted that some components of the computer system  100  can be omitted in some non-limiting embodiments of the present technology. For example, the touchscreen  190  can be omitted, especially (but not limited to) where the computer system is implemented as a server. 
     According to implementations of the present technology, the solid-state drive  120  stores program instructions suitable for being loaded into the random-access memory  130  and executed by the processor  110  and/or the GPU  111 . For example, the program instructions may be part of a library or an application. 
     Networked Computing Environment 
     With reference to  FIG.  2   , there is depicted a schematic diagram of a networked computing environment  200  suitable for use with some non-limiting embodiments of the systems and/or methods of the present technology. The networked computing environment  200  comprises a server  202  communicatively coupled, via a communication network  208 , to an electronic device  204 . In the non-limiting embodiments of the present technology, the electronic device  204  may be associated with a user  216 . 
     In some non-limiting embodiments of the present technology, the electronic device  204  may be any computer hardware that is capable of running a software appropriate to the relevant task at hand. Thus, some non-limiting examples of the electronic device  204  may include personal computers (desktops, laptops, netbooks, etc.), smartphones, and tablets. It should be expressly understood that, in some non-limiting embodiments of the present technology, the electronic device  204  may not be the only electronic device associated with the user  216 ; and the user  216  may rather be associated with other electronic devices (not depicted in  FIG.  2   ) having access to the online search platform  210  via the communication network  208  without departing from the scope of the present technology. 
     In some non-limiting embodiments of the present technology, the server  202  is implemented as a conventional computer server and may comprise some or all of the components of the computer system  100  of  FIG.  1   . In a specific non-limiting example, the server  202  is implemented as a Dell™ PowerEdge™ Server running the Microsoft™ Windows Server™ operating system, but can also be implemented in any other suitable hardware, software, and/or firmware, or a combination thereof. In the depicted non-limiting embodiments of the present technology, the server  202  is a single server. In alternative non-limiting embodiments of the present technology (not depicted), the functionality of the server  202  may be distributed and may be implemented via multiple servers. 
     In some non-limiting embodiments of the present technology, the server  202  can be configured to host an online search platform  210 . Broadly speaking, the online search platform  210  denotes a web software system configured for conducting searches in response to submitting search queries thereto. Types of search results the online search platform  210  can be configured to provide in response to the search queries generally depend on a particular implementation of the online search platform  210 . For example, in some non-limiting embodiments of the present technology, the online search platform  210  can be implemented as a search engine (such as a Google™ search engine, a Yandex™ search engine, and the like), and the search results may include digital documents of various types, such as, without limitation, audio digital documents (songs, voice recordings, podcasts, as an example), video digital documents (video clips, films, cartoons, as an example), text digital documents, and the like. Further, in some non-limiting embodiments of the present technology, the online search platform  210  may be implemented as an online listing platform (such as a Yandex™ Market™ online listing platform), and the search results may include digital documents including advertisements of various items, such as goods and services. Other implementations of the online search platform  210  are also envisioned. 
     Therefore, in some non-limiting embodiments of the present technology, the server  202  can be communicatively coupled to a search database  206  configured to store information of digital documents potentially accessible via the communication network  208 , for example, by the electronic device  204 . To that end, the search database  206  could be preliminarily populated with indications of the digital documents, for example, via the process known as “crawling”, which, for example, can be implemented, in some non-limiting embodiments of the present technology, also by the server  202 . In additional non-limiting embodiments of the present technology, the server  202  can be configured to store, in the search database  206 , data indicative of every search conducted by the user  216  on the online search platform  210 , and more specifically, search queries and respective sets of digital documents responsive thereto as well as their metadata, as an example. 
     Further, although in the embodiments depicted in  FIG.  2   , the search database  206  is depicted as a single entity, it should be expressly understood that in other non-limiting embodiments of the present technology, the functionality of the search database  206  could be distributed among several databases. Also, in some non-limiting embodiments of the present technology, the search database  206  could be accessed by the server  202  via the communication network  208 , and not via a direct communication link (not separately labelled) as depicted in  FIG.  2   . 
     Thus, the user  216 , using the electronic device  204 , may submit a given query  212  to the online search platform  210 , and the online search platform  210  can be configured to identify, in the search database  206 , a set of digital documents  214  responsive to the given query  212 . Further, to aid the user  216  in navigating through the set of digital documents  214 , digital documents therein may need to be ranked, for example, according to their respective degrees of relevance to the given query  212 . 
     In some non-limiting embodiments of the present technology, such degrees of relevance of each one of the set of digital documents  214  to the given user  216  may be represented by respective likelihood values of the given user  216  interacting with each one of the set of digital documents  214 . For example, according to some non-limiting embodiments of the present technology, interacting with a given digital document may include at least one of: (i) the user  216  making at least one click on the given digital document, (ii) the user  216  making a long click on the given digital document, such as when the user  216  remains in the given digital document from a predetermined period (for example, 120 seconds); (iii) the user  216  dwelling on the given digital document within the set of digital document  214  for a predetermined period; and the like. It should be expressly understood that other types of user interactions of the given user  216  with digital documents are also envisioned without departing from the scope of the present technology. 
     In some non-limiting embodiments of the present technology, to determine the respective likelihood values for each one of the set of digital documents  214 , the server  202  can be configured to train and further apply a machine-learning algorithm (MLA)  218 . Generally speaking, the server  202  can be said to be executing two respective processes in respect of the MLA  218 . A first process of the two processes is a training process, where the server  202  is configured to train the MLA  218 , based on a training set of data, to determine the respective likelihood values of the user  216  interacting with digital documents in the set of digital documents  214 , which will be discussed below with reference to  FIGS.  3  to  5   . A second process is an in-use process, where the server  202  executes the so-trained MLA  218  for respective likelihood values, which will be described further below, in accordance with certain non-limiting embodiments of the present technology. 
     Developers of the present technology have appreciated that determining the respective likelihood values for each of the set of digital documents  214  may be more efficient and/or accurate if the MLA  218  is trained akin to natural language processing MLAs configured to determine missing tokens (such as words, phonemes, syllables, and the like) in a text based on a context provided by neighboring tokens therein. Thus, in some non-limiting embodiments of the present technology, the MLA  218  could be implemented as a Transformer-based MLA, such as a BERT MLA, architecture of which as well as generating the training set of data therefor will be described, in accordance with certain non-limiting embodiments of the present technology, below with reference to  FIGS.  3  to  5   . 
     Communication Network 
     In some non-limiting embodiments of the present technology, the communication network  208  is the Internet. In alternative non-limiting embodiments of the present technology, the communication network  208  can be implemented as any suitable local area network (LAN), wide area network (WAN), a private communication network or the like. It should be expressly understood that implementations for the communication network are for illustration purposes only. How a respective communication link (not separately numbered) between each one of the server  202  and the electronic device  204  and the communication network  208  is implemented will depend, inter alia, on how each one of the server  202  and the electronic device  204  is implemented. Merely as an example and not as a limitation, in those embodiments of the present technology where the electronic device  204  is implemented as a wireless communication device such as a smartphone, the communication link can be implemented as a wireless communication link. Examples of wireless communication links include, but are not limited to, a 3G communication network link, a 4G communication network link, and the like. The communication network  208  may also use a wireless connection with the server  202 . 
     Machine Learning Model Architecture 
     With reference to  FIG.  3   , there is depicted a block diagram of an architecture of the MLA  218 , in accordance with certain non-limiting embodiments of the present technology. As noted above, in some non-limiting embodiments of the present technology, the MLA  218  can be based on the BERT machine learning model, as described, for example, in the Devlin et al. paper referenced above. Like BERT, the MLA  218  includes a transformer stack  302  of transformer blocks, including, for example, transformer blocks  304 ,  306 , and  308 . 
     Each of the transformer blocks  304 ,  306 , and  308  includes a transformer encoder block, as described, for example, in the Vaswani et al. paper, referenced above. Each of the transformer blocks  304 ,  306 , and  308  includes a multi-head attention layer  320  (shown only in the transformer block  304  here, for purposes of illustration) and a feed-forward neural network layer  322  (also shown only in transformer block  304 , for purposes of illustration). The transformer blocks  304 ,  306 , and  308  are generally the same in structure, but (after training) will have different weights. In the multi-head attention layer  320 , there are dependencies between the inputs to the transformer block, which may be used, for example, to provide context information for each input based on each other input to the transformer block. The feed-forward neural network layer  322  generally lacks these dependencies, so the inputs to the feed-forward neural network layer  322  may be processed in parallel. It will be understood that although only three transformer blocks (transformer blocks  304 ,  306 , and  308 ) are shown in  FIG.  2   , in actual implementations of the disclosed technology, there may be many more such transformer blocks in the transformer stack  302 . For example, some implementations may use 12 transformer blocks in the transformer stack  302 . 
     Inputs  330  to the transformer stack  302  include tokens, such as a [CLS] token  332 , and tokens  334 . The tokens  334  may, for example represent words or portions of words. The [CLS] token  332  is used as a representation for classification for the entire set of tokens  334 . Each of the tokens  334  and the [CLS] token  332  is represented by a vector. In some implementations, these vectors may each be, for example, 768 floating point values in length. It will be understood that a variety of compression techniques may be used to effectively reduce sizes (dimensionality) of the vectors. In some non-limiting embodiments of the present technology, there may be a fixed number of the tokens  334  that are used as the inputs  330  to the transformer stack  302 . For example, in some non-limiting embodiments of the present technology, 1024 tokens may be used, while in other implementations, the transformer stack  302  may be configured to take 512 tokens (aside from the [CLS] token  332 ). Those of the inputs  330  that are shorter than this fixed number of tokens  334  may be extended to the fixed length by adding padding tokens, as an example. 
     In some implementations, the inputs  330  may be generated from a training digital object  336 , such as at least one of a past digital document and a past query associated therewith, as will be described below, using a tokenizer  338 . The architecture of the tokenizer  338  will generally depend on the training digital object  336  that serve as input to the tokenizer  338 . For example, in some non-limiting embodiments of the present technology, the tokenizer  338  may involve use of known encoding techniques, such as byte-pair encoding, as well as use of pre-trained neural networks for generating the inputs  330 . 
     However, in other non-limiting embodiments of the present technology, the tokenizer  338  can be implemented based on a WordPiece byte-pair encoding scheme, such as that used in BERT learning models with a sufficiently large vocabulary size. For example, in some non-limiting embodiments of the present technology, the vocabulary size may be approximately 120,000 tokens. In some non-limiting embodiments of the present technology, before applying the tokenizer  338 , the inputs  330  can be preprocessed. For example, all words of the inputs  330  can be converted lowercase and Unicode NFC normalization can further be performed. The WordPiece byte-pair encoding scheme that may be used in some implementations to build the token vocabulary is described, for example, in Rico Sennrich et al., “Neural Machine Translation of Rare Words with Subword Units”,  Proceedings of the  54 th Annual Meeting of the Association for Computational Linguistics  ( Volume  1:  Long Papers ), pages 1715-1725, 2016. 
     Outputs  350  of the transformer stack  302  include a [CLS] output  352 , and a vector of outputs  354 , including a respective output value for each of the tokens  334  in the inputs  330  to the transformer stack  302 . The outputs  350  may then be sent to a task module  370 . In some implementations, as is depicted in  FIG.  3   , the task module  370  uses only the [CLS] output  352 , which serves as a representation of the entire vector of the outputs  354 . This can be most useful when the task module  370  is being used as a classifier, or to output a label or value that characterizes the entire input training digital object  336 , such as generating a relevance score—for example, the respective likelihood value of the user  216  interacting with the given digital document described above. In some non-limiting embodiments of the present technology (not depicted in  FIG.  3   ) all or some values of the vector of the outputs  354 , and possibly the [CLS] output  352  may serve as inputs to the task module  370 . This can be most useful when the task module  370  is being used to generate labels or values for each one of the tokens  334  of the inputs  330 , such as for prediction of a masked or missing token or for named entity recognition. In some non-limiting embodiments of the present technology, the task module  370  may include a feed-forward neural network (not depicted) that generates a task-specific result  380 , such as a relevance score or click probability. Other models could also be used in the task module  370 . For example, the task module  370  may itself be a transformer or other form of neural network. Additionally, the task-specific result  380  may serve as an input to other models, such as a CatBoost model, as described in Dorogush et al., “CatBoost: gradient boosting with categorical features support”,  NIPS  2017. 
     It will be understood that the architecture of the MLA  218  described above with reference to  FIG.  3    has been simplified for ease of clarity and understanding of certain non-limiting embodiments of the present technology. For example, in an actual implementation of the MLA  218 , each of the transformer blocks  304 ,  306 , and  308  may also include layer normalization operations, the task module  370  may include a softmax normalization function, and so on. One of ordinary skill in the art would understand that these operations are commonly used in neural networks and deep learning models such as the MLA  218 . 
     Training Process 
     According to certain non-limiting embodiments of the present technology, the server  202  can be configured to retrieve training data and based thereon train the MLA  218  to determine the respective likelihood values of the user  216  interacting with each one of the set of digital documents  214 . 
     With reference to  FIG.  4   , there is depicted a schematic diagram of training data  402  associated with the user  216  and one of approaches of organizing it for training the MLA  218 , in accordance with certain non-limiting embodiments of the present technology. 
     In some non-limiting embodiments of the present technology, the training data  402  can include data of past searches conducted by the user  216  using the online search platform  210 . For example, the server  202  can be configured to retrieve, over the communication network  208 , the data of past searches conducted by the user  216  from at least one electronic device associated therewith, such as the electronic device  204  described above. However, in other non-limiting embodiments of the present technology, the server  202  can be configured to retrieve the data of the past searches from the search database  206 . Further, in some non-limiting embodiments of the present technology, the training data  402  can include data of a predetermined number of past searches the user  216  has conducted hitherto, such as 256 or 128, as an example. However, in other non-limiting embodiments of the present technology, the training data  402  can include data of the past searches the user  216  has conducted over a predetermined period, such as one month, one week, and the like. 
     More specifically, in some non-limiting embodiments of the present technology, the training data  402  can include a plurality of past queries submitted by the user  216  to the online search platform  210 , such as a given past query  404 . Further, for the given past query  404 , the training data  402 , can further include a respective set of past digital documents  406  generated by the online search platform  210  in response to receiving the given past query  404 . Further, a given past digital document  408  of the respective set of past digital documents  406  includes a label  410  indicative of past user interaction of the user  216  with the given past digital document  408  upon receiving the respective set of past digital documents  406 . 
     As noted hereinabove, the given past digital document  408  can include electronic media content entities of various formats and types that are suitable for being transmitted, received, stored, and displayed on the electronic device  204  using suitable software, such as a browser, as an example. 
     According to some non-limiting embodiments of the present technology, the past user interaction of the user  216  in respect of the given past digital document  408  may include at least one of: (i) a click of the user  216  on the given past digital document  408 ; (ii) a long click on the given past digital document  408 , that is remaining in the given past digital document  408  after clicking thereon for a predetermined period (such as 120 seconds); (iii) dwelling on the given past digital document  408  over a predetermined period (such as 10 seconds), as an example. 
     Thus, the label  410  may take a binary value, such as one of ‘1’ (or ‘Positive’) if the user  216  has interacted with (such as clicked on) the given past digital documents  408 , and ‘0’ (or ‘Negative’) if the user  216  has not interacted with the given past digital document  408  upon receiving the respective set of past digital documents  406 . 
     In additional non-limiting embodiments of the present technology, the given past query  404  can further include query metadata (not depicted), such as a geographical region from which the user  216  submitted the given past query  404 , and the like. Similarly, the given past digital document  408  can further include document metadata (not depicted), such as a title thereof, a web address thereof (for example, in the form of a URL), as an example. 
     Further, in some non-limiting embodiments of the present technology, the server  202  can be configured to train the MLA  218  to determine the respective likelihood values of the user  216  interacting with each one of the set of digital documents  214  described above in two phases. More specifically, during a first training phase, the server  202  can be configured to train the MLA  218  for determining if the user  216  has interacted with the given past digital document  408 , that is for determining the value of the label  410  associated therewith. Further, during a second training phase, the server  202  can be configured to train the MLA  218  to determine respective likelihood values of the user  216  interacting with in-use digital documents, such as each one of the set of digital documents  214 , while having access to weights generated in the first training phase. More specifically, during the first training phase, the server  202  can be said to determine initial weights of the transformer blocks  304 ,  306 , and  308 , as described above; and, during the second training phase, the server  202  can be configured to finetune the so determined initial weights of the transformer blocks  304 ,  306 , and  308  of the MLA  218 . 
     Thus, for training the MLA  218 , for each one of the first and second training phase, the server  202  can be configured to organize the training data  402  in two different training sets of data as will be described below. 
     In some non-limiting embodiments of the present technology, for training the MLA  218  during the first training phase, the server  202  can be configured to organize the training data  402  in a first set of training digital objects  420 , as further depicted in  FIG.  4   . 
     A given one of the first set of training digital objects  420  includes: (i) the given past query  404  and (ii) a first set of past digital documents  422 . According to certain non-limiting embodiments of the present technology, each one of the first set of past digital documents  422  is selected from the respective set of past digital documents  406  having been generated by the online search platform  210  in response to the user  216  submitting the given past query  404 , however, without data of respective labels associated therewith, such as the label  410  associated with the given past digital document  408 . In other words, during the first training phase, the MLA  218  is not aware of the value of the label  410 , and is trained for predicting it based on context provided by at least one of the given past digital document  408  associated therewith and the given past query  404 . 
     It should be expressly understood that it is not limited how each one of the first set of past digital documents  422  has been selected from the respective set of past digital documents  406 ; and in some non-limiting embodiments of the present technology, the first set of past digital documents  422  may include all past digital documents of the respective set of past digital documents  406 . However, in some non-limiting embodiments of the present technology, the first set of past digital documents  422  may include a predetermined number of past digital documents from the respective set of past digital documents  406 , such as three, five, or twenty, as an example. In other non-limiting embodiments of the present technology, the server  202  can be configured to select each one of the predetermined number of training digital objects from the respective set of past digital documents  406  randomly, such as based on a predetermined distribution, such as normal, as an example. In yet other non-limiting embodiments of the present technology, the server  202  can be configured to select each one of the predetermined number of training digital objects from the respective set of past digital documents  406  as being positioned at preselected positions within the respective set of past digital documents  406 , such as fifth, tenth, thirty-second, and the like. 
     Further, as noted above with reference to  FIG.  3   , using the tokenizer  338 , the server  202  can be configured to convert the given one of the first set of training digital objects  420  in a respective token and feed it to the MLA  218  as part of the inputs  330  for training the MLA  218  to determine the values of the respective labels associated with each one of the first set of past digital documents  422  of the first set of training digital objects  420 , that is, whether the user  216  has interacted therewith or not. 
     Thus, organization of the training data  402  in the first set of training digital objects  420  provides the MLA  218  with more tokens in the inputs  330 , for which the MLA  218  is trained for generating respective value of the vector of outputs  354 , thereby determining initial weights of the transformer blocks  304 ,  306 , and  308 . For example, the initial weights can be determined and further adjusted based on a difference or a distance between predicted values of the respective labels associated with each one of the first set of past digital documents  422  and ground truth, that is, actual values thereof obtained as part of the training data  402 . For example, the server  202  can be configured to determine the difference using a loss function, such as a Cross-Entropy Loss function, as an example, and further adjust the initial weights of the transformer blocks  304 ,  306 , and  308  to minimize the difference between the predicted and actual values of the respective labels. 
     It should be expressly understood that other implementations of the loss function are also envisioned by the non-limiting embodiments of the present technology and may include, by way of example, and not as a limitation, a Mean Squared Error Loss function, a Huber Loss function, a Hinge Loss function, and others. 
     Further, with reference to  FIG.  5   , there is depicted a schematic diagram of the server  202  organizing the training data  402  into a second set of training digital objects  520  for training the MLA  218  during the second training phase, in accordance with certain non-limiting embodiments of the present technology. 
     According to certain non-limiting embodiments of the present technology, a given one of the second set of training digital objects  520  includes (i) the given past query  404  and (ii) a second set of past digital documents  522  having been selected, by the server  202 , from the respective set of past digital documents  406 . In some non-limiting embodiments of the present technology, the server  202  can be configured to select each one of the second set of past digital documents  522  as having a predetermined value of a respective user interaction therewith represented by associated labels, such as the value of the label  410  associated with the given past digital document  408 . For example, in some non-limiting embodiment of the present technology, the server  202  can be configured to select only those from the respective set of past digital documents  406  that have positive values of the respective labels associated therewith for inclusion in the second set of past digital documents  522 , such as a positive label  526  associated with an other given past digital document  524 . In other words, in these embodiments, the server  202  can be configured to include only those past digital documents with which the user  216  has interacted—such as clicked thereon, as an example. 
     In some non-limiting embodiment of the present technology, a total number of training digital objects in the second set of training digital object  520  could be equal to that of the first set of training digital objects  420 . However, in those embodiments of the present technology where the training data  402  includes respective sets of past digital documents where the user  216  did not interact with any one of past digital documents thereof, the total number of training digital objects in the second set of training digital objects  520  could be smaller than that of the first set of training digital objects  420 . 
     In yet other non-limiting embodiments of the present technology, the total numbers in each one of the first set of training digital objects  420  and the second set of training digital objects  520  could be predetermined and comprise, for example,  100 ,  200 , or  300  training digital objects as described above with reference to  FIGS.  4  and  5   , respectively. 
     Further, akin to the first training phase, the server  202  can be configured to convert each one of the second set of training digital objects  520  in a token using the tokenizer  338  and feed the so generated tokens to the MLA  218 , thereby training the MLA  218  to determine likelihood values of the user  216  interacting with in-use digital documents, such as the set of digital documents  214  generated in response to the user  216  having submitted the given query  212 . 
     Further, in some non-limiting embodiments of the present technology, the server  202  can be configured to use the so generated likelihood values of the user  216  interacting with the in-use digital documents and respective positive labels associated with each past digital document in the second set of training digital objects  520  to determine a difference therebetween using the loss function as described above. Further, the server  202  can be configured to minimize the difference, thereby adjusting the initial weights the transformer blocks  304 ,  306 , and  308  determined in the first training phase. 
     Thus, with the so adjusted weights of the transformer blocks  304 ,  306 , and  308 , the server  202  can be configured to use the MLA  218  to determine the respective likelihood values of the user  216  interacting with the in-use digital documents, such as the set of digital documents  214  generated in response to the user  216  having submitted the given query  212  as described above with reference to  FIG.  2   . 
     In-Use Process 
     According to certain non-limiting embodiments of the present technology, during the in-use process, the server  202  can be configured to receive the set of digital documents  214 . Further, the server  202  can be configured to organize the set of digital documents  214  into a set of in-use digital objects, a given in-use digital object of which includes (i) the given query  212  and (ii) and a respective digital document of the set of digital documents  214 . In additional non-limiting embodiments of the present technology, the given in-use digital objects may include metadata associated with the given query  212  and document metadata associated with each one of the set of digital documents  214 , as described above. 
     Further, the server  202  can be configured to tokenize, such as by the tokenizer  338  described above, each one of the set of in-use digital objects and provide the resulting tokens as the inputs  330  to the MLA  218 . Thus, based on the context provided by neighboring tokens in the inputs  330 , the MLA  218  may be configured to predict, for a given token, a respective likelihood value of the user  216  interacting with a respective one of the set of digital documents  214  associated with the given token. 
     Further, the server  202  could be configured to use the so determined respective likelihood values for ranking the set of digital documents  214 . To that end, in some non-limiting embodiments of the present technology, the server  202  can be configured to provide the respective likelihood values determined by the MLA  218  as an input to an other MLA (not depicted) that has been configured to rank digital documents based at least on associated respective likelihood values of a given user, such as the user  216 , interacting therewith. In some non-limiting embodiments of the present technology, the other MLA can comprise an ensemble of CatBoost decision trees as mentioned above. The other MLA may thus generate a ranked set of digital documents. 
     Further, the server  202  can be configured to select an N-top digital documents from the ranked set of digital documents for transmitting indications thereof to the electronic device  204  of the user  216 , such as within a respective client interface (not depicted) of the online search platform  210 . 
     Method 
     Given the architecture and the examples provided hereinabove, it is possible to execute a method for training an MLA to rank digital documents, such as the MLA  218  described above. With reference now to  FIG.  6   , there is depicted a flowchart diagram of a method  600 , according to certain non-limiting embodiments of the present technology. The method  600  may be executed by the server  202 .
     STEP  602 : RECEIVING, BY THE PROCESSOR, TRAINING DATA ASSOCIATED WITH A GIVEN USER, THE TRAINING DATA INCLUDING (I) A PLURALITY OF PAST QUERIES HAVING BEEN SUBMITTED BY THE GIVEN USER TO THE ONLINE SEARCH PLATFORM; (II) RESPECTIVE SETS OF PAST DIGITAL DOCUMENTS GENERATED, BY THE ONLINE SEARCH PLATFORM, IN RESPONSE TO SUBMITTING THERETO EACH ONE OF THE PLURALITY OF PAST QUERIES, AND A GIVEN PAST DIGITAL DOCUMENT INCLUDING A RESPECTIVE PAST USER INTERACTION PARAMETER INDICATIVE OF WHETHER THE GIVEN USER HAS INTERACTED WITH THE GIVEN PAST DIGITAL DOCUMENT   

     At step  602 , according to certain non-limiting embodiments of the present technology, the server  202  could be configured to retrieve the training data  402  associated with the user  216  for training the MLA  218 . 
     According to some non-limiting embodiments of the present technology, the MLA  218  may include a Transformer-based MLA, such as the BERT MLA, the architecture of which is described above with reference to  FIG.  3   . 
     As mentioned above with reference to  FIG.  4   , the training data  402  may include: (1) the plurality of past queries submitted by the user  216  to the online search platform  210 ; (2) respective sets of past digital documents, such as the respective set of past digital documents  406  generated by the online search platform  210  in response to receiving the given past query  404 , wherein (3) the given past digital document  408  of the respective set of past digital documents  406  includes the label  410  indicative of past user interaction of the user  216  with the given past digital document  408  upon receiving the respective set of past digital documents  406 . 
     In additional non-limiting embodiments of the present technology, the given past query  404  can further include query metadata (not depicted), such as a geographical region from which the user  216  submitted the given past query  404 , and the like. Similarly, the given past digital document  408  can further include document metadata (not depicted), such as a title thereof, a web address thereof (for example, in the form of a URL), as an example. 
     For example, in some non-limiting embodiments of the present technology, the server  202  could be configured to retrieve the training data  402  from the electronic device  204  associated with the user  216  over the communication network  208 . However, in other non-limiting embodiments of the present technology, the server  202  can be configured to retrieve the training data  402  from the search database  206  communicatively coupled thereto. 
     The method  600  thus proceeds to step  604 .
     STEP  604 : ORGANIZING, BY THE PROCESSOR, THE TRAINING DATA IN A FIRST SET OF TRAINING DIGITAL OBJECTS, A GIVEN TRAINING DIGITAL OBJECT OF THE FIRST SET OF TRAINING DIGITAL OBJECTS INCLUDING: (I) A RESPECTIVE PAST QUERY FROM THE PLURALITY OF PAST QUERIES; AND (II) A PREDETERMINED NUMBER OF PAST DIGITAL DOCUMENTS RESPONSIVE TO THE RESPECTIVE PAST QUERY   

     Further, at step  604 , the server  202  can be configured to organize the training data  402  into the first set of training digital objects  420  for training the MLA  218  during the first training phase for determining past user interactions of the user  216  with each past digital document of the training data  402 , such as the given past digital document  408 . 
     As noted above with reference to  FIG.  4   , the given one of the first set of training digital objects  420  includes: (i) the given past query  404  and (ii) the first set of past digital documents  422  having been selected from the respective set of past digital documents  406 . Each one of the first set of past digital documents  422  is selected from the respective set of past digital documents  406 , however, without data of respective labels associated therewith, such as the label  410  associated with the given past digital document  408 . 
     The method  600  hence advances to step  606 .
     STEP  606 : TRAINING, BY THE PROCESSOR, BASED ON THE FIRST SET OF TRAINING DIGITAL OBJECTS, THE MLA FOR DETERMINING, FOR THE GIVEN TRAINING DIGITAL OBJECT OF THE FIRST SET OF TRAINING DIGITAL OBJECTS, IF THE GIVEN USER HAS INTERACTED WITH EACH ONE OF THE PREDETERMINED NUMBER OF PAST DIGITAL DOCUMENTS   

     Thus, as described above with joint reference to  FIGS.  3  and  4    using the first set of training digital objects  420 , the server  202  can be configured to train the MLA  218  for determining the respective likelihood values of the user  216  interacting with each one of the first set of past digital documents  422  associated with the given one of the first set of training digital objects  420 . 
     More specifically, the server  202  can be configured to convert the given one of the first set of training digital objects  420  in a respective token and feed it to the MLA  218  as part of the inputs  330  for training the MLA  218  for determining the values of the respective labels associated with each one of the first set of past digital documents  422  of the first set of training digital objects  420 , that is, whether the user  216  has interacted therewith or not. 
     In other words, during the first training phase, the MLA  218  is not aware of the values of the respective labels associated with each one of the first set of past digital documents  422 , and is trained for predicting them based on context provided by each of the past documents themselves as well as the given past query  404  used for generation thereof. 
     The method  600  hence proceeds to step  608 .
     STEP  608 : ORGANIZING, BY THE PROCESSOR, THE TRAINING DATA IN A SECOND SET OF TRAINING DIGITAL OBJECTS, A GIVEN TRAINING DIGITAL OBJECT OF THE SECOND SET OF TRAINING DIGITAL INCLUDING: (I) THE RESPECTIVE PAST QUERY FROM THE PLURALITY OF PAST QUERIES; AND (II) A NUMBER OF PAST DIGITAL DOCUMENTS RESPONSIVE TO THE RESPECTIVE TRAINING WITH WHICH THE GIVEN USER HAS INTERACTED   

     At step  608 , as described above with reference to  FIG.  5   , the server  202  can be configured to organize the training data  402  into the second set of training digital objects  520  for training the MLA  218  during the second training phase. 
     More specifically, as mentioned further above with reference to  FIG.  5   ,the given one of the second set of training digital objects  520  includes (i) the given past query  404  and (ii) the second set of past digital documents  522  having been selected, by the server  202 , from the respective set of past digital documents  406  as having positive values of the respective labels associated therewith. 
     The method  600  hence advances to step  610 .
     STEP  610 : TRAINING, BY THE PROCESSOR, BASED ON THE SECOND SET OF TRAINING DIGITAL OBJECTS, THE MLA TO DETERMINE, FOR A GIVEN IN-USE DIGITAL DOCUMENT, A LIKELIHOOD PARAMETER OF THE GIVEN USER INTERACTING WITH THE GIVEN IN-USE DIGITAL DOCUMENT   

     Thus, having generated the second set of training digital objects  520 , the server  202  can be configured to train the MLA  218  to determine the respective likelihood values of the user  216  interacting with in-use digital documents, such as those of the set of digital documents  214 , as described above with joint reference to  FIGS.  3  and  5   , similar to the first training phase. 
     Further, after the training the MLA  218 , the server  202  can be configured to use it to determine the respective likelihood values of the user  216  interacting with each one of the set of digital documents  214  by organizing it into the in-use set of digital objects as described above and feed the in-use set of digital objects to the MLA  218 . 
     Further, the server  202  can be configured to se the respective likelihood values for ranking each one of the set of digital objects  214 . To that end, in some non-limiting embodiments of the present technology, the server  202  can be configured to provide the respective likelihood values determined by the MLA  218  as an input to the other MLA (not depicted) that has been configured to rank digital documents based at least on associated respective likelihood values of a given user, such as the user  216 , interacting therewith. In some non-limiting embodiments of the present technology, the other MLA can comprise the ensemble of CatBoost decision trees as mentioned above. 
     Further, the server  202  can be configured to select an N-top digital documents from the ranked set of digital documents for transmitting indications thereof to the electronic device  204  of the user  216 , such as within a respective client interface (not depicted) of the online search platform  210 . 
     Thus, certain non-limiting embodiments of the method  600  allow improving quality of personalized ranking of digital documents. 
     The method  600  hence terminates. 
     It will also be understood that, although the embodiments presented herein have been described with reference to specific features and structures, various modifications and combinations may be made without departing from such disclosures. For example, various optimizations that have been applied to neural networks, including transformers and/or BERT may be similarly applied with the disclosed technology. Additionally, optimizations that speed up in-use relevance determinations may also be used. For example, in some implementations, the transformer model may be split, so that some of the transformer blocks are split between handling a query and handling a document, so the document representations may be pre-computed offline and stored in a document retrieval index. 
     The specification and drawings are, accordingly, to be regarded simply as an illustration of the discussed implementations or embodiments and their principles as defined by the appended claims, and are contemplated to cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure.