Patent Publication Number: US-2018047095-A1

Title: Automated product personalization based on multiple sources of product information

Description:
TECHNICAL FIELD OF THE INVENTION 
     The present disclosure relates generally to the field of natural language processing, and more particularly to product-customer matching based on using natural language processing to generate a product personality profile. 
     BACKGROUND OF THE INVENTION 
     Product personalization, such as in the context of targeted product promotion in a retail setting, can be a manual process whereby a catalog manager creates one or more sales (product) categories to which one or more customer segments can be assigned. The sales manager can assign a customer segment to a sales category with the goal of designing a product promotion that will appeal to that segment based on ideals or characteristics associated with the segment. 
     Natural language processing is a field of artificial intelligence that is concerned with the understanding and/or generation of human language, such as but not limited to human-readable text, by computers. Natural language processing typically employs machine learning algorithms to perform various tasks, such as but without limitation parsing of textual information (e.g., breaking a string of words into constituent pieces for an analytical purpose). Natural language processing can be used to associate ideals and characteristics with textual information. 
     SUMMARY 
     Embodiments of the present invention disclose a computer-implemented method for automated product personalization based on product personality, the computer-implemented method comprising: receiving, by one or more computer processors, textual information related to a product, wherein the textual information comprises one or more of an initial product description, purchase history information, a product review, a user survey, product attribute information, a product name, a catalog hierarchy, and a product category description; generating, by the one or more computer processors, a set of product personality traits based on analyzing the textual information by a natural language processor; identifying, by the one or more computer processors, a set of customer personality traits for a target customer group, wherein the target customer group is defined based on a user input; determining, by the one or more computer processors, a degree of correlation between a first trait from the set of product personality traits and a second trait from the set of customer personality traits; determining, by the one or more computer processors, whether the degree of correlation meets or exceeds a predetermined threshold value, wherein the predetermined threshold value is based on the user input; revising, by the one or more computer processors, responsive to determining that the degree of correlation does not meet or exceed the predetermined threshold value, the textual information based on a psycholinguistic dictionary; extracting, by the one or more computer processors, one or more words from the textual information; determining, by the one or more computer processors, that the one or more words do not exist in the psycholinguistic dictionary; adding, by the one or more computer processors, the one or more words to the psycholinguistic dictionary; determining, by the one or more computer processors, one or more replacement words in the psycholinguistic dictionary for one or more words in the textual information; replacing, by the one or more computer processors, the one or more words in the textual information with the one or more replacement words to generate revised textual information; generating, by the one or more computer processors, a revised set of product personality traits based on analyzing the revised textual information by a natural language processor; determining, by the one or more computer processors, a degree of correlation between a first trait from the set of revised product personality traits and a second trait from the set of customer personality traits; determining, by the one or more computer processors, whether the degree of correlation meets or exceeds the predetermined threshold value; and repeating, by the one or more computer processors, until the degree of correlation meets or exceeds the predetermined threshold value, the revising. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an exemplary computing environment, in accordance with an embodiment of the present invention; 
         FIG. 2  is a flowchart depicting steps of a method for automated product personalization based on product personality, in accordance with an embodiment of the present invention; 
         FIGS. 3A and 3B  are implementation diagrams showing generation and revision of a product personality profile, in accordance with embodiments of present invention; and 
         FIG. 4  is a block diagram of components of the computing device in  FIG. 1  executing a program for automated product personalization based on product personality, in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Current approaches to product personalization can be time-consuming, e.g., in the case of manual creation of sales categories and assignment of customers to those categories, and the resulting assignments can be sub-optimal if not validated. Embodiments disclosed herein propose a product-personality-based approach that can increase the strength of product-customer matching (and thus, e.g., assist in the tailoring of targeted product descriptions) based on repeatedly evaluating and updating a product personality profile. 
     Embodiments of the present invention are described herein with reference to the Figures.  FIG. 1  shows a block diagram of a computing environment  100 , in accordance with an embodiment of the present invention.  FIG. 1  is provided for the purposes of illustration and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made by those skilled in the art without departing from the scope of the invention as recited in the claims. 
     Computing environment  100  includes computing device  104 , which can be interconnected with other devices (not shown) over network  102 . Network  102  can be, for example, a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, or a combination of these, and can include wired, wireless, or fiber optic connections. In general, network  102  can be any combination of connections and protocols that will support communications between computing device  104  and other computing devices (not shown) within computing environment  100 . 
     Computing device  104  can be any programmable electronic device capable of executing machine-readable instructions and communicating with other devices over network  102 , in accordance with an embodiment of the present invention. Computing device  104  includes product information  106 , personality trait inference component  112 , product personality traits  116 , customer personality traits  118 , personalization component  120 , and psycholinguistic dictionary  122 . Computing device  104  can include internal and external hardware components, as depicted and described in further detail with reference to  FIG. 4 . 
     Product information  106  is textual information that can be received by a natural language processor (NLP) for the purpose of producing a set of product personality traits (also referred to as “traits” or a “product personality profile,” e.g., product personality traits  116 , described herein). Product information  106  can include managed information  108  and customer-generated information  110 . 
     Managed information  108  comprises information provided by a user. Managed information  108  can be, for example but without limitation, a product description written by a catalog manager in human-readable text, product attribute information, a product name, a catalog hierarchy, and a product category description. Personalization component  120  can revise managed information  108  based on psycholinguistic dictionary  122 , as described herein. 
     Customer-generated information  110  comprises information derived from customer-generated content. Customer-generated information  110  can be, for example but without limitation, product purchase history information, including information about customers who have previously viewed and/or purchased the product; and product reviews, customer comments, and other customer-generated content describing the product. 
     Personality trait inference component  112  can be based on any NLP with the ability to receive textual information, i.e., product information  106 , and produce product personality traits  116 , in accordance with an embodiment of the present invention. Personality trait inference component  112  includes natural language processing component  114 , which can be, for example but without limitation, based on Watson Personality Insights by International Business Machines Corporation (IBM). 
     In an exemplary embodiment, personality trait inference component  112  can receive product information  106  pursuant to separate requests made to one or more data sources. For example, personality trait inference component  112  can make a first request for a product description, a second request for product attributes, and a third request for product reviews. 
     In an exemplary embodiment, personality trait inference component  112  can produce product personality traits  116  by tokenizing product information  106  to produce a set of keywords and determining a set of personality traits  116  based on matching the keywords with traits in psycholinguistic dictionary  122  (described herein) by, for example but without limitation, a weighted combination approach. Tokenization is a natural language processing technique well-known to those skilled in the art that produces a set of semantic elements based on textual input. The weighted combination approach comprises computing one or more traits based on word counts in one or more Linguistic Word Count (LIWC) categories correlated with the trait(s), e.g., based on a set of weights or correlation coefficients established through research, such as in the work of Yarkoni. For example, personality trait inference component  112  can parse product information  106  into LIWC categories, determine a word count for each category, and determine a product using appropriate correlation coefficients for respective traits. In the same example, personality trait inference component  112  can compute a dot product of a vector containing word counts for LIWC categories and a vector containing the appropriate correlation coefficients. 
     In an exemplary embodiment, personality trait inference component  112  can take a set of percentage scores determined pursuant to separate requests for product information  106  (e.g., a first request for a product description, a second request for product attributes, and a third request for product reviews) and compute an average, i.e., arithmetic mean, for each trait in order to produce one set of percentage scores per trait in product personality traits  116 . 
     Product personality traits  116  comprise one or more personality traits associated with a product. Product personality traits  116  can be, for example but without limitation, traits according to the Big Five (or “five factor”) model (wherein Openness, Conscientiousness, Extraversion, Agreeableness, and Emotional Range/Neuroticism, are Big Five personality traits), a Needs model (e.g., wherein Stability is a Need) based on the work of Kotler and Ford, and/or a Values model (e.g., wherein Self-Transcendence is a Value) based on the work of Schwartz. In an exemplary embodiment, product personality traits  116  can comprise percentage scores and sub-scores for traits according to the Big Five model. For example, product personality traits  116  can comprise a 90% score for Conscientiousness and various sub-scores for Achievement Striving, Cautiousness, Dutifulness, Orderliness, Self-Discipline, and Self-Efficacy. 
     Customer personality traits  118  comprise customer personality traits for one or more target customer segments that are based on, for example but without limitation, user input and/or analysis of customer-generated information  110  by personality trait inference component  112 . Customer personality traits  118  can be, for example but without limitation, traits according to the Big Five model, a Needs model, and/or a Values model. 
     Personalization component  120  receives product personality traits  116  and customer personality traits  118 , determines a degree of correlation between product personality traits  116  and customer personality traits  118 , compares the degree of correlation to a threshold value, and, if the degree of correlation does not meet or exceed the threshold value, revises product personality traits  116  based on revising product information  106 . 
     In an exemplary embodiment, personalization component  120  can determine a degree of correlation based on respective percentage scores of an Active trait in product personality traits  116  and an Active trait in customer personality traits  118 . For example, personalization component  120  can compute the absolute difference in percentage scores between the corresponding traits, wherein a smaller absolute difference indicates a closer match. For example, if an Active trait in product personality traits  116  has a score of 90% (i.e., 0.90) and an Active trait in customer personality traits  118  has a score of 78% (i.e., 0.78), personalization component  120  can compute a difference of 0.12. In the same example, personalization component  120  can determine a differences in percentage scores for each pair of traits in personality traits  116  and customer personality traits  118 . 
     The threshold value can be defined and/or revised based on a user input (e.g., human input, merchandizer input). In accordance with the afore-described example, the user can define a desired threshold value of 90% or 0.90, wherein the threshold value can be computed based on subtracting a difference in percentage scores from a perfect correlation (i.e., 1.0). In the same example, the computed difference of 12% can be subtracted from a perfect correlation, resulting in a value of 88% (computed as 1.0−0.12=0.88), which falls below the defined threshold value of 90%. 
     Personalization component  120  continues to revise product personality traits  116  and evaluate the degree of correlation between product personality traits  116  and customer personality traits  118  until the threshold value is met or exceeded, as described herein with reference to  FIG. 2 . In an example, personalization component  120  can compute an initially large difference that decreases as personalization component  120  continues to revise product personality traits  116  and evaluate the degree of correlation. 
     Psycholinguistic dictionary  122  can be accessed by personality trait inference component  112  for the purpose of generating product personality traits  116 , and by personalization component  120  for the purpose of revising managed information  108 . Psycholinguistic dictionary  122  is a natural language (i.e., human-language) dictionary wherein one or more keywords are associated with one or more personality traits. A keyword can be, for example but without limitation, an adjective such as “sporty” or “quick.” A personality trait can be, for example but without limitation, an Athletic trait. In an exemplary embodiment, the keywords “sporty” and “quick” can be associated with the Athletic trait in psycholinguistic dictionary  122 . 
     In an exemplary embodiment, personalization component  120  can revise managed information  108  by replacing one or more keywords in a product description (e.g., managed information  108 ) based on psycholinguistic dictionary  122 . Personalization component  120  can, for example but without limitation, replace one or more keywords in managed information  108  that is not associated with a desired trait in psycholinguistic dictionary  122  with one or more keywords in managed information  108  that is associated with a desired trait in psycholinguistic dictionary  122 , as further described herein with reference to  FIG. 3A . 
     Psycholinguistic dictionary  118  can be enriched based on personality trait inference component  112  extracting terms from customer-generated information  110 . In an example, customer-generated information  110  includes the following product review for running shoes: “I love how flexible, breathable, and durable these shoes are! These are very comfortable!” In the same example, personality trait inference component  112  can extract the following adjectives from customer-generated information  110 : “flexible,” “breathable,” “durable,” and “comfortable.” Personality trait inference component  112  can add the adjective “breathable” to psycholinguistic dictionary  122  based on determining that “breathable” is not yet associated with running shoes in psycholinguistic dictionary  122 . For example, a search for the adjective “breathable” can return “fit or pleasant to breathe.” A further search for “fit or pleasant to breathe” can return “Breathing Exercises.” Based on the results, personality trait inference component  112  can determine a level of confidence for associating the adjective “breathable” with an Active trait and add “breathable” to psycholinguistic dictionary  122 , if “breathable” is not yet associated with the Active trait. In an embodiment, analogous steps can be performed by a user (e.g., a catalog manager) to enrich psycholinguistic dictionary  122 . 
       FIG. 2  is a flowchart  200  depicting operational steps of a method for automated product personalization based on product personality, in accordance with an embodiment of the present invention. 
     In step  202 , personality trait inference component  112  receives product information  106  from one or more sources. 
     In step  204 , personality trait inference component  112  determines one or more product personality traits  116  based on product information  106 . 
     In step  206 , personalization component  120  identifies one or more customer personality traits  118 . 
     In step  208 , personalization component  120  determines a degree of correlation between one or more traits from product personality traits  116  and one or more traits from customer personality traits  118 . 
     In step  210 , personalization component  120  determines whether the degree of correlation meets or exceeds, or does not meet or exceed, a threshold value. 
     If the degree of correlation does not meet or exceed the threshold value in step  210  (NO branch, step  210 ), then in step  212 , personalization component  120  revises managed information  108  and repeats steps  204 - 210 . 
       FIGS. 3A and 3B  are implementation diagrams representing generation and revision of a product personality profile, in accordance with exemplary embodiments of the present invention. 
       FIG. 3A  shows an example  300  of generation and revision of a product personality profile for product  302 . Product information  106  (not illustrated) can be, for example but without limitation, managed information  108  comprising a product description, product attributes, product names, a catalog hierarchy, and category descriptions, and customer-generated information  110  comprising purchase history information, product reviews, and user surveys for product  302 . Personality trait inference component  112  receives product information  106 . Personality trait inference component  112  determines product personality traits  116  for product  302 . In update process  304 , responsive to a degree of correlation between product personality traits  116  and customer personality traits  118  (not illustrated) not meeting and not exceeding a threshold value, personalization component  120  (not illustrated) alters product information  106 . 
     For example, product information  106  comprises a product description containing the keywords “comfortable” and “supportive.” Based on product information  106 , product personality traits  116  comprise a high percentage score in a Modest trait and a low percentage score in an Active trait. If in this example a merchandizer interacting with the system defines a target customer segment, represented by customer personality traits  118 , that scores high in an Active trait (e.g., if the target customer segment comprises athletes or outdoorspeople), and the degree of correlation between product personality traits  116  and customer personality traits  118  does not meet or exceed a threshold value defined by the merchandizer, personalization component  120  alters managed information  108  in update process  304  to include the adjectives “performance-enhancing” and “dynamic” in order to increase the percentage score for the Active trait in product personality traits  116 . 
       FIG. 3B  shows an example  350  of generation and revision of a product personality profile for running shoes  352 . Example  350  shows a more specific implementation of the components and steps illustrated in example  300 , wherein personality trait inference component  112  is based on IBM Watson Personality Insights  354 . Example  350  is intended to be non-limiting, and natural language processing component  114  can be a different NLP in another embodiment. 
       FIG. 4  depicts a block diagram  400  of components of a computer, such as computing device  104  in computing environment  100 , in accordance with illustrative embodiments of the present invention. It should be appreciated that  FIG. 4  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment can be made. 
     Block diagram  400  shows communications fabric  402 , which provides communications between computer processor(s)  404 , memory  406 , persistent storage  408 , communications unit  410 , and input/output (I/O) interface(s)  412 , and cache  414 . Communications fabric  402  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  402  can be implemented with one or more buses. 
     Memory  406  and persistent storage  408  are computer readable storage media. In this embodiment, memory  406  includes random access memory (RAM) and cache memory  414 . In general, memory  406  can include any suitable volatile or non-volatile computer readable storage media. Cache  414  is a fast memory that enhances the performance of computer processor(s)  404  by holding recently accessed data, and data near accessed data, from memory  406 . 
     Program instructions and data used to practice embodiments of the invention, referred to collectively as component(s)  416 , are stored in persistent storage  408  for execution and/or access by one or more of the respective computer processors  404  via one or more memories of memory  406 . In this embodiment, persistent storage  408  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  408  can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  408  may also be removable. For example, a removable hard drive can be used for persistent storage  408 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  408 . 
     Communications unit  410 , in these examples, provides for communications with other data processing systems or devices. Communications unit  410  can include one or more network interface cards. Communications unit  410  can provide communications through the use of either or both physical and wireless communications links. Component(s)  416  can be downloaded to persistent storage  408  through communications unit  410 . 
     I/O interface(s)  412  allows for input and output of data with other devices that may be connected to computing device  104 . For example, I/O interface  412  can provide a connection to external devices  418  such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices  418  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., component(s)  416 , can be stored on such portable computer readable storage media and can be loaded onto persistent storage  408  via I/O interface(s)  412 . I/O interface(s)  412  also connect to a display  420 . 
     Display  420  provides a mechanism to display data to a user and may be, for example, a touchscreen. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.