WO2019061664A1 - Dispositif électronique, procédé de recommandation de produit basé sur des données de navigation sur internet d'un utilisateur et support d'enregistrement - Google Patents

Dispositif électronique, procédé de recommandation de produit basé sur des données de navigation sur internet d'un utilisateur et support d'enregistrement Download PDF

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Publication number
WO2019061664A1
WO2019061664A1 PCT/CN2017/108789 CN2017108789W WO2019061664A1 WO 2019061664 A1 WO2019061664 A1 WO 2019061664A1 CN 2017108789 W CN2017108789 W CN 2017108789W WO 2019061664 A1 WO2019061664 A1 WO 2019061664A1
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Prior art keywords
user
keyword table
service keyword
product recommendation
predetermined
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PCT/CN2017/108789
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English (en)
Chinese (zh)
Inventor
刘睿恺
吴振宇
王建明
肖京
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries

Definitions

  • the present invention relates to the field of Internet product recommendation, and in particular, to an electronic device and a product recommendation method based on user internet data.
  • the present invention provides an electronic device and a product recommendation method based on user Internet data, which can automatically complete product recommendation of the user according to the user's online data, and improve recommendation efficiency and accuracy.
  • a first aspect of the present invention provides a product recommendation method based on user internet data, and the method includes the following steps:
  • the second business keyword table corresponding to each of the other users is generated, wherein the business keyword includes a verb describing a user's online behavior and a noun describing the product, and the business keyword table includes the same user at the first preset All business keywords during the time;
  • the predetermined terminal sends a product recommendation instruction for the user.
  • a second aspect of the present invention provides an electronic device including a memory, a processor, and a user-based data-based product stored on the memory and operable on the processor
  • the recommendation system when the product recommendation system based on the user's Internet data is executed by the processor, implements the following steps:
  • the first service keyword table and the second service keyword table corresponding to the similar user are analyzed according to a predetermined user preference product recommendation model, and the product preferred by the user is determined, and sent to a predetermined terminal. Product recommendation instructions for this user.
  • a third aspect of the present invention provides a computer readable storage medium, where the computer readable storage medium stores a product recommendation system based on user internet data, and the product recommendation system based on user internet data
  • the at least one processor can be executed by the at least one processor to perform the following steps:
  • the first service keyword table and the second service keyword table corresponding to the similar user are analyzed according to a predetermined user preference product recommendation model, and the product preferred by the user is determined, and sent to a predetermined terminal. Product recommendation instructions for this user.
  • the electronic device, the product recommendation method based on the user's Internet access data, and the computer readable storage medium obtain the Internet data of each user within a preset time from a predetermined database;
  • the predetermined business keyword table generation rule analyzes the obtained online data of each user to generate a service keyword table corresponding to each user;
  • the product preference recommendation model corresponding to the user is analyzed according to the predetermined user preference product recommendation model to determine the product corresponding to the preference of the user, and the product recommendation instruction for the user preference is sent to the predetermined terminal.
  • FIG. 1 is a schematic diagram of an optional hardware architecture of an electronic device of the present invention
  • FIG. 2 is a schematic diagram of a program module of a product recommendation program based on user internet data in an implementation of an electronic device according to the present invention
  • FIG. 3 is a schematic flow chart of an implementation of a preferred embodiment of a product recommendation method based on user internet data according to the present invention.
  • first, second and the like in the present invention are for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of indicated technical features. .
  • features defining “first” and “second” may include at least one of the features, either explicitly or implicitly.
  • the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. It is also within the scope of protection required by the present invention.
  • FIG. 1 it is a schematic diagram of an optional application environment of the electronic device of the present invention.
  • the electronic device 1 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus. It should be noted that FIG. 1 only shows the electronic device 1 having the components 11-13, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, SD or DX memory, etc.), a random access memory (RAM), and a static random access.
  • Memory SRAM
  • ROM read only memory
  • EEPROM electrically erasable programmable read only memory
  • PROM programmable read only memory
  • magnetic memory magnetic disk, optical disk, and the like.
  • the memory 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1.
  • the memory 11 is also It may be an external storage device of the electronic device 1, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC), a Secure Digital (SD) card, a flash card, or the like.
  • the memory 11 can also include both an internal storage unit of the electronic device 1 and an external storage device thereof.
  • the memory 11 is generally used to store an operating system installed on the electronic device 1 and various types of application software, such as a product recommendation program based on user Internet data. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 12 is generally used to control the overall operation of the electronic device 1.
  • the processor 12 is configured to run program code or processing data stored in the memory 11, such as a running product recommendation program based on user Internet data. .
  • the network interface 13 may include a wireless network interface or a wired network interface, and the network interface 13 is generally used to establish a communication connection between the electronic device 1 and other electronic devices.
  • the device may further include a user interface
  • the user interface may include a display
  • an input unit such as a keyboard
  • the optional user interface may further include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like.
  • the display may also be appropriately referred to as a display screen or a display unit for displaying information processed in the electronic device and a user interface for displaying visualization.
  • the memory 11 stores a product recommendation program based on the user's Internet data.
  • the processor 12 executes the product recommendation program based on the user's Internet data stored in the memory 11 , the following steps are implemented:
  • the first service keyword table and the second service keyword table corresponding to the similar user are analyzed according to a predetermined user preference product recommendation model, and the product preferred by the user is determined, and sent to a predetermined terminal. Product recommendation instructions for this user.
  • the online data of the user within a preset time may be obtained according to the user's identification information, such as a login name, a registered user name, a mobile phone number, and the like. And won
  • the received Internet data is data for recording the user's real behavior. For example, at 2:40 pm on March 27, 2017, the user named A clicks the button of the certificate to send the traffic activity, and downloads the certificate APP.
  • the user's behavior and preferred products can be analyzed according to the words with actual business meaning in the data recording the user's real behavior. For example, the verb can be clicked and downloaded according to the above-mentioned real behavior data to describe the user's online behavior, and the nouns describing the product.
  • the first online data of the user within the first preset time (for example, one week) is first obtained from a predetermined database (the user) The real online behavior data), and other individual users (for example, other users of the predetermined preset number of users except the other users), the second Internet data in the first preset time (other individual users) Real online behavior data); then, using the predetermined business keyword table generation rule to analyze the obtained first Internet data, generating a first service keyword table corresponding to the user, and acquiring the second Internet data Performing an analysis to generate a second service keyword table corresponding to each of the other users, where the predetermined service keyword table generation rule includes:
  • Constructing a set of service keywords in the online data of the preset number of users in the second preset time specifically, in an embodiment, constructing the business keywords in the Internet data of 300 users within two months
  • the process of forming the set includes pre-acquiring the online data of 300 users in two months, and extracting keywords with actual business meanings from the obtained online data, wherein the keywords have actual business meanings.
  • Including verbs that describe the user's online behavior eg, click, download, purchase, etc.
  • nouns describing the product eg, food, credit card, wealth management products, etc.
  • the set of nouns is a collection of business keywords constructed to meet the above requirements.
  • the business keyword generates a corresponding first business keyword table; similarly, the second online data traverses the set of the business keyword to obtain the business keyword in the set composed of the business keyword from the second online data.
  • Each of the second service keywords generates a corresponding second service keyword table based on the acquired second service keywords.
  • the predetermined similarity analysis rule may be a cosine angle similarity method, a Euclidean distance metric method, or a Pearson correlation coefficient method.
  • the Euclidean distance metric is taken as an example, wherein the Euclidean distance metric uses the following formula to calculate the similarity:
  • x and y in the above formula are respectively normalized vectors, and the result of the above formula is the similarity between the vector x and the vector y.
  • the user and other users are calculated.
  • the first service vector table corresponding to the user is normalized according to a preset normalization manner to obtain the first vector x in the above formula, and the second service corresponding to each other user is obtained.
  • a second vector y different from the above formula is obtained, and then the first vector x and the other second vectors y are respectively substituted into the above similarity calculation.
  • a formula that calculates a similarity between the user and each of the other users, and compares the calculated similarity with a preset similarity threshold, and if the calculated similarity is greater than a preset similarity threshold, Determined to be a similar user for this user.
  • the predetermined user preference product recommendation model is a collaborative filtering recommendation model
  • the training process of the user preference product recommendation model includes the following steps:
  • the user preference product recommendation model is trained by using a second business keyword table of each similar user in the training set to obtain a trained user preference product recommendation model;
  • the user preference product recommendation model is tested by using the second service keyword table of each similar user in the test set. If the test passes, the training ends, or if the test fails, the second business keyword of the similar user in the training set is increased.
  • the table samples and re-execute the steps described above for training the user preference product recommendation model.
  • the steps of testing the user preference product recommendation model using the second business keyword table of each similar user in the test set include:
  • a similar user's preference probability value for the preset type product is greater than a preset preference probability threshold, the model accuracy test is performed for the similar user, and the user is confirmed to pay attention to the preset type product from the online data of the similar user.
  • a frequency value if the similar user pays attention to the frequency value of the preset type product exceeds a preset frequency threshold, determining that the model accuracy test result for the similar user is correct, or if the similar user pays attention to the preset type product If the frequency value is equal to or less than the preset frequency threshold, it is determined that the model accuracy test result for the similar user is an error;
  • the test for the user preference product recommendation model is determined, or if the correct model accuracy test results account for all models If the percentage of the accuracy test result is less than or equal to the preset percentage threshold, it is determined that the test for the user preference product recommendation model fails.
  • the first service keyword table and the second service keyword table are analyzed by using a predetermined clustering algorithm to analyze the second service.
  • the users corresponding to the keyword table belong to the same user group.
  • the predetermined clustering algorithm includes a density-based clustering algorithm, and the specific process is: pre-setting a density hierarchy (for example, in the embodiment, the first service
  • the keyword list includes different preset types of business keywords corresponding to different density levels, a highest density threshold (eg, including 6 preset types of business keywords), and a minimum density threshold (eg, including 3 pre- Set the type of business keyword), and according to the set density level, the highest density threshold, and the lowest density threshold, analyze the density levels included in each second business keyword table, and the density thresholds included in each density level, and analyze The density thresholds are arranged according to the density threshold from high to low, the highest density threshold is selected, and all preset types of business keywords are clustered for the first time based on the selected density threshold to generate cluster clusters.
  • the residual density threshold repeatedly performs the above clustering process until the ith clustering is performed to generate a clustering family; wherein the clustering cluster generated by the i-th clustering process can only be expanded in the subsequent clustering process and cannot be Segmentation or merging into other clusters; and clusters that satisfy the current density threshold are preferentially extracted; the algorithm is for all preset classes in turn Keywords business at different cluster density threshold, the i-th cluster results directly as input i + 1-time clustering, until the remaining threshold value is less than the density of the lowest density threshold, then the algorithm ends. So far, the user corresponding to the first service keyword table corresponding to each user in the second service keyword table is clustered by the user group.
  • the product recommendation program based on the user's Internet data may be divided into one or more program modules, and one or more program modules are stored in the memory 11 and processed by one or more
  • the processor which is processor 12 in this embodiment
  • a program module as used herein refers to a series of computer program instructions that are capable of performing a particular function.
  • FIG. 2 it is a schematic diagram of a product recommendation program module based on user internet data in an embodiment of the electronic device of the present invention.
  • the product recommendation program based on the user's Internet access data may be divided into an acquisition module 201, a first analysis module 202, a second analysis module 203, and a third analysis module 204.
  • the functions or operation steps implemented by the program modules 201-204 in this embodiment are similar to the above, and are not described in detail herein, for example, where:
  • the obtaining module 201 is configured to: if the product is recommended to the user with the identification information, obtain the first online data of the user in the first preset time from the predetermined database, and the other users are in the first pre-preparation Set the second online data in time;
  • the first analysis module 302 is configured to analyze the obtained first Internet data by using a predetermined service keyword table generation rule, generate a first service keyword table corresponding to the user, and generate the second Internet data. Performing an analysis to generate a second service keyword table corresponding to each of the other users, wherein the service keyword includes a verb describing the user's online behavior and a noun describing the product, and the business keyword table includes all services of the same user within the first preset time. Key words;
  • the second analysis module 303 is configured to analyze the first service keyword table and the second service keyword table according to the predetermined similarity analysis rule to analyze the similar user corresponding to the user;
  • the third analysis module 304 is configured to analyze the first service keyword table and the second service keyword table corresponding to the similar user according to the predetermined user preference product recommendation model, to determine the product that the user prefers, to a predetermined
  • the terminal sends a product recommendation instruction for the user.
  • the present invention also provides a product recommendation method based on user online data.
  • FIG. 3 it is a schematic flowchart of an implementation example of a preferred example of a product recommendation method based on user Internet data according to the present invention. The method can be performed by a device that can be implemented by software and/or hardware.
  • the product recommendation method based on the user's online data includes:
  • Step S301 If the product needs to be recommended to the user with the identification information, the first online data of the user in the first preset time and the other users in the first preset time are obtained from the predetermined database. Second internet data;
  • Step S302 analyzing the acquired first Internet data by using a predetermined business keyword table generation rule, generating a first service keyword table corresponding to the user, and analyzing the acquired second Internet data to generate other each a second service keyword table corresponding to the user, wherein the service keyword includes a verb describing the user's online behavior and a noun describing the product, and the business keyword table includes all the business keywords of the same user within the first preset time;
  • Step S303 analyzing the first service keyword table and the second service keyword table according to the predetermined similarity analysis rule, to analyze the similar user corresponding to the user;
  • Step S304 analyzing the first service keyword table and the second service keyword table corresponding to the similar user according to the predetermined user preference product recommendation model, to determine the product preferred by the user, and sending the predetermined user to the predetermined terminal.
  • Product recommendation instructions analyzing the first service keyword table and the second service keyword table corresponding to the similar user according to the predetermined user preference product recommendation model, to determine the product preferred by the user, and sending the predetermined user to the predetermined terminal.
  • the online data of the user within a preset time may be obtained according to the user's identification information, such as a login name, a registered user name, a mobile phone number, and the like.
  • the obtained online data is data for recording the real behavior of the user. For example, at 2:40 pm on March 27, 2017, the user with the user name A clicks the button of the card to send the traffic activity, and downloads the certificate APP.
  • the product that has the actual business meaning in the data that records the user's real behavior can be used to analyze the user's behavior and preferred products. For example, the verb can be clicked and downloaded according to the above-mentioned real behavior data to describe the user's online behavior, and the product description is described.
  • Nouns, APPs, traffic, and buttons to analyze the user's behavior and preferences of the product, so if you can extract words with actual business meaning from the online data of the recorded user's real behavior, you can get the Words with actual business meaning effectively analyze the products that the user prefers, and recommend related products to the user according to the products preferred by the user.
  • the first online data of the user within the first preset time (for example, one week) is first obtained from a predetermined database (the user) The real online behavior data), and other individual users (for example, other users of the predetermined preset number of users except the other users), the second Internet data in the first preset time (other individual users) Real online behavior data); then, using the predetermined business keyword table generation rule to analyze the obtained first Internet data, generating a first service keyword table corresponding to the user, and acquiring the second Internet data Performing an analysis to generate a second service keyword table corresponding to each of the other users, where the predetermined service keyword table generation rule includes:
  • Constructing a set of service keywords in the online data of the preset number of users in the second preset time specifically, in an embodiment, constructing the business keywords in the Internet data of 300 users within two months
  • the process of composing the collection includes pre-acquisition of 300 users online within two months.
  • Keywords having actual business meanings include verbs describing user online behavior (eg, clicking, downloading, purchasing, etc.), and Descriptive product nouns (eg, food, credit cards, wealth management products, etc.), the set of extracted verbs describing the user's online behavior, and the nouns describing the product are the set of business keywords that satisfy the above requirements. .
  • Traversing the set of the business keyword obtaining the first words matching the business keywords in the set of the business keywords from the first online data, and generating the corresponding first service based on the acquired first words Keyword table; similarly, traversing a set of business keyword sets, obtaining second words matching the business keywords in the set of business keywords from the second internet data, and based on the acquired second words The word generates a corresponding second business keyword table.
  • the predetermined similarity analysis rule may be a cosine angle similarity method, a Euclidean distance metric method, or a Pearson correlation coefficient method.
  • the Euclidean distance metric is taken as an example, wherein the Euclidean distance metric uses the following formula to calculate the similarity:
  • x and y in the above formula are respectively normalized vectors, and the result of the above formula is the similarity between the vector x and the vector y.
  • the user and other users are calculated.
  • the first service vector table corresponding to the user is normalized according to a preset normalization manner to obtain the first vector x in the above formula, and the second service corresponding to each other user is obtained.
  • a second vector y different from the above formula is obtained, and then the first vector x and the other second vectors y are respectively substituted into the above similarity calculation.
  • a formula that calculates a similarity between the user and each of the other users, and compares the calculated similarity with a preset similarity threshold, and if the calculated similarity is greater than a preset similarity threshold, Determined to be a similar user for this user.
  • the predetermined user preference product recommendation model is a collaborative filtering recommendation model
  • the training process of the user preference product recommendation model includes the following steps:
  • the user preference product recommendation model is trained by using a second business keyword table of each similar user in the training set to obtain a trained user preference product recommendation model;
  • the user preference product recommendation model is tested by using the second service keyword table of each similar user in the test set. If the test passes, the training ends, or if the test fails, the second business keyword of the similar user in the training set is increased.
  • the table samples and re-execute the steps described above for training the user preference product recommendation model.
  • a similar user's preference probability value for the preset type product is greater than a preset preference probability threshold, the model accuracy test is performed for the similar user, and the user is confirmed to pay attention to the preset type product from the online data of the similar user.
  • a frequency value if the similar user pays attention to the frequency value of the preset type product exceeds a preset frequency threshold, determining that the model accuracy test result for the similar user is correct, or if the similar user pays attention to the preset type product If the frequency value is equal to or less than the preset frequency threshold, it is determined that the model accuracy test result for the similar user is an error;
  • the test for the user preference product recommendation model is determined, or if the correct model accuracy test results account for all models If the percentage of the accuracy test result is less than or equal to the preset percentage threshold, it is determined that the test for the user preference product recommendation model fails.
  • the first service keyword table and the second service keyword table are analyzed by using a predetermined clustering algorithm to analyze the Among the users corresponding to the two service keyword tables, the users corresponding to the first service keyword table belong to the same user group.
  • the predetermined clustering algorithm includes a density-based clustering algorithm, and the specific process is: pre-setting a density level (for example, in the embodiment, the first service keyword table includes different preset types of business keywords.
  • the number corresponds to setting different density levels), the highest density threshold (for example, including 6 preset types of business keywords), and the lowest density threshold (for example, including 3 preset types of business keywords), and according to the settings
  • the density level, the highest density threshold, and the lowest density threshold analyze the density levels contained in each of the second business keyword tables, and the density thresholds included in each density level, and the respective density thresholds to be analyzed are based on the density threshold Low arrangement, selecting the highest density threshold and performing the first clustering on all preset types of business keywords based on the selected density threshold, generating a cluster cluster, and repeating the remaining density threshold to perform the clustering process until proceeding
  • the i-th cluster generates a cluster family; wherein the cluster cluster generated by the i-th cluster process is in the subsequent clustering process Can only be extended and cannot be split or merged into other clusters; and clusters that satisfy the current density threshold are preferentially extracted; the algorithm sequentially clusters all preset types of business keywords at different density thresholds, i The result of the sub-
  • the embodiment of the present invention further provides a computer readable storage medium, where the product recommendation program based on the user's Internet data is stored, and the product recommendation program based on the user's Internet data is executed by the processor as follows: operating:
  • the first online data collected by the user is analyzed by using a predetermined business keyword table generation rule, and the first service keyword table corresponding to the user is generated, and the acquired second online data is analyzed to generate corresponding addresses of other users.
  • a second service keyword table wherein the service keyword includes a verb describing a user's online behavior and a noun describing the product, and the business keyword table includes all business keywords of the same user in the first preset time ;
  • the specific embodiment of the computer readable storage medium of the present invention is substantially the same as the above embodiments of the electronic device and the product recommendation method based on the user's Internet access data, and will not be described herein.
  • the electronic device of the present invention, the product recommendation method based on the user's Internet access data, and the computer readable storage medium utilize the advance data by acquiring the Internet data of each user within a preset time period from a predetermined database.
  • the determined business keyword generation rule analyzes the obtained online data to generate a business keyword table corresponding to each user; analyzes the business keyword table according to the predetermined similarity analysis rule to analyze the corresponding user Similar user; analyzing the first service keyword table corresponding to the user and the second service keyword table corresponding to the similar user according to the predetermined user preference product recommendation model, to determine the product preferred by the user, to the predetermined terminal Send a product recommendation instruction for this user. It can automatically complete the product recommendation of the user's preference according to the user's online data, and improve the recommendation efficiency and accuracy.

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Abstract

La présente invention concerne un procédé de recommandation de produit basé sur des données de navigation sur Internet d'un utilisateur. Ledit procédé consiste à acquérir, à partir d'une base de données prédéfinie, des données de navigation sur Internet de chaque utilisateur dans un temps prédéfini ; utiliser une règle de génération de table de mots-clés de service prédéfinie pour analyser les données de navigation sur Internet acquises de chaque utilisateur, de façon à générer une table de mots-clés de service correspondant à chaque utilisateur ; et analyser, selon un modèle de recommandation de produit préféré d'utilisateur prédéfini, la table de mots-clés de service correspondant à l'utilisateur, de façon à déterminer un produit préféré correspondant à l'utilisateur et envoyer, à un terminal prédéfini, une instruction de recommandation de produit pour la préférence d'utilisateur. De cette manière, la présente invention peut non seulement éviter les inconvénients en l'état de la technique d'une charge de travail lourde provoquée par l'analyse nécessaire de données redondantes de masse ayant une faible densité de valeur, mais également améliorer l'efficacité de travail et la précision de recommandation de produit.
PCT/CN2017/108789 2017-09-30 2017-10-31 Dispositif électronique, procédé de recommandation de produit basé sur des données de navigation sur internet d'un utilisateur et support d'enregistrement WO2019061664A1 (fr)

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CN110895588B (zh) * 2018-09-13 2022-07-22 中国移动通信有限公司研究院 一种数据处理方法及设备
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