WO2020073534A1 - Procédé et appareil de poussée basés sur le re-clustering, dispositif informatique et support d'enregistrement - Google Patents

Procédé et appareil de poussée basés sur le re-clustering, dispositif informatique et support d'enregistrement Download PDF

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WO2020073534A1
WO2020073534A1 PCT/CN2018/125334 CN2018125334W WO2020073534A1 WO 2020073534 A1 WO2020073534 A1 WO 2020073534A1 CN 2018125334 W CN2018125334 W CN 2018125334W WO 2020073534 A1 WO2020073534 A1 WO 2020073534A1
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vector
product
user
row vector
commodity
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PCT/CN2018/125334
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Chinese (zh)
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吴壮伟
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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
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques

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  • the present application relates to the field of information push technology, and in particular, to a push method, device, computer equipment, and storage medium based on re-clustering.
  • the recommendation algorithm based on collaborative filtering is commonly used in the user-rating matrix.
  • the user-rating matrix represents the user's rating of the item (the item can be understood as a specific product).
  • the horizontal axis of the user-rating matrix is the item, and the vertical axis is the user.
  • the value is user i's rating of item j. As the amount of product data becomes larger, the cost of maintaining a full user-scoring matrix scoring system will become higher and higher.
  • the embodiments of the present application provide a push method, device, computer equipment and storage medium based on re-clustering, aiming to solve the scoring system corresponding to the full user-scoring matrix of online malls in the prior art as the number of commodities increases, More and more bloated, leading to the difficulty of maintaining a full user-scoring matrix.
  • an embodiment of the present application provides a push method based on re-clustering, which includes:
  • a commodity recommendation list is obtained from the commodity corresponding to the comprehensive score value in the commodity recommendation row vector before the preset second ranking threshold, and the commodity recommendation list is pushed to the receiving end corresponding to the target user.
  • an embodiment of the present application provides a re-clustering-based pushing device, which includes:
  • the user clustering unit is used for clustering the obtained user-scoring matrix through DBSCAN clustering to obtain at least one cluster group and a sub-user-scoring matrix corresponding to each cluster group in one-to-one correspondence;
  • the cluster judgment unit is used to obtain the cluster group corresponding to the target user's row vector according to the target user corresponding to the selected row vector in the sub-user-score matrix;
  • the similar user rating matrix acquisition unit is used to calculate and obtain the Euclidean distance between each rating row vector and the target user's row vector in the cluster group corresponding to the target user, and obtain the preset first rank in each Euclidean distance
  • the scoring row vector corresponding to the Euclidean distance before the threshold to form a scoring matrix for similar user groups;
  • the product recommendation row vector acquisition unit is used to obtain the comprehensive rating value of each product by the similar user group based on each rating row vector in the similar user group rating matrix to form a product recommendation row vector;
  • the information pushing unit is used to obtain the product recommendation list from the products corresponding to the comprehensive rating value whose rating rank is before the preset second ranking threshold in the product recommendation row vector, and push the product recommendation list to the receiving end corresponding to the target user .
  • an embodiment of the present application further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer
  • the program implements the push method based on the re-clustering described in the first aspect.
  • an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor causes the processor to execute the first On the one hand, the push method based on re-clustering.
  • FIG. 1 is a schematic flowchart of a push method based on re-clustering provided by an embodiment of the present application
  • FIG. 2 is a schematic diagram of a sub-process of a push method based on re-clustering provided by an embodiment of the present application
  • FIG. 3 is a schematic diagram of another sub-process of a push method based on re-clustering provided by an embodiment of the present application;
  • FIG. 4 is a schematic block diagram of a re-clustering-based pushing device provided by an embodiment of the present application.
  • FIG. 5 is a schematic block diagram of a subunit of a re-clustering-based push device provided by an embodiment of the present application
  • FIG. 6 is a schematic block diagram of another subunit of a re-clustering-based push device provided by an embodiment of the present application.
  • FIG. 7 is a schematic block diagram of a computer device provided by an embodiment of the present application.
  • FIG. 1 is a schematic flowchart of a re-clustering-based push method provided by an embodiment of the present application.
  • the re-clustering-based push method is applied to a management server.
  • the method is implemented by application software installed in the management server.
  • the management server is the enterprise terminal for pushing based on re-clustering.
  • the method includes steps S110-S150.
  • the user-score matrix represents the user's rating of the product (items can be understood as specific products), the horizontal axis of the user-score matrix is the item, and the vertical axis is the user, and the value in it is user i to item j 'S rating.
  • the user-score matrix S is a 4 ⁇ 5 matrix, such as:
  • the row vector in the first row of the user-scoring matrix S represents user 1 ’s ratings for products 1-commodity 5
  • the row vector in the second row represents user 2 ’s ratings for products 1-commodity 5
  • the third row ’s The row vectors represent user 3's ratings for products 1-5 respectively
  • the fourth row row vectors represent user 4 ratings for products 1-5 respectively.
  • the DBSCAN clustering model is used to cluster the row vectors in the user-scoring matrix, so that similar users are divided into the same cluster group according to the user's score for each product, and each user groups each product in the same cluster group
  • the ratings are approximate (that is, the difference between the ratings of the products is small).
  • step S110 includes:
  • minPts which represents the minimum number of included points
  • ⁇ neighborhood which means the area within the scan radius of the given object, centered on the given object
  • Core object means that if the number of objects included in the ⁇ neighborhood of a given object is greater than or equal to the minimum number of inclusion points, the given object is called a core object;
  • the direct density is reachable, which means that for the sample set D, if the sample point q is in the ⁇ neighborhood of p, and p is the core object, then the object q is directly reachable from the object p;
  • the density is reachable, then the object q is reachable from the object p density;
  • the density is connected, which means that there is a point o in the sample set D. If the objects o to p and q are all reachable in density, then p and q are connected in density.
  • DBSCAN Density-Based Spatial Clustering of Applications with Noise
  • DBSCAN requires two parameters: scanning radius (eps) and minimum included points (minPts). Pick an unvisited point to start, and find all nearby points that are within the distance of eps (including eps).
  • the current point forms a cluster with its nearby points, and the departure point is marked as visited. Then recursively, process all points in the cluster that are not marked as visited in the same way, so as to expand the cluster. If the number of nearby points ⁇ minPts, the point is temporarily marked as a noise point. If the cluster is fully expanded, that is, all points in the cluster are marked as visited, then use the same algorithm to deal with the points that have not been visited.
  • the cluster group to which the row vector belongs is obtained at the same time. You can quickly determine the similar users of the target user.
  • the Euclidean distance between each rating row vector in the cluster group and the target user's row vector can be obtained, and each Euclidean distance can be After sorting in descending order, the score row vector corresponding to the Euclidean distance ranked before the preset first ranking threshold in each Euclidean distance is acquired to form a similar user group scoring matrix. If the first ranking threshold is set to 11, then the scoring row vector corresponding to the Euclidean distance ranked 1-10 in each Euclidean distance is obtained, and these 10 row vectors form a similar user group scoring matrix. After acquiring similar users of the target user, calculation can be performed with a small number of selected row vectors, without calling the full user-scoring matrix, which reduces the amount of calculation in the running process.
  • step S130 includes:
  • the rating row vectors of the similar users are sequentially obtained.
  • the above method can accurately obtain the rating rows corresponding to the similar users
  • a similar user group scoring matrix composed of vectors is convenient for subsequent calculation of the user's comprehensive scoring value for each product.
  • the comprehensive user's comprehensive score value can be calculated for each product.
  • the product recommendation row vector can be calculated, and the product recommendation row vector is used as the basis for product recommendation.
  • step S140 includes:
  • a first cluster group and a second cluster group are obtained, where the first cluster group includes the rating row vectors of user 1 and user 2,
  • the two-cluster cluster includes user 3 and user 4's rating row vectors.
  • the sub-user-score matrix corresponding to the first cluster group is composed of user 1 and user 2 score row vectors
  • the sub-user-score matrix corresponding to the second cluster group is composed of user 3 and user 4 score row vectors.
  • the cluster group corresponding to the rating row vector of user 1 is the first cluster group.
  • the first cluster group also includes the rating of user 2 Line vector.
  • the similar user group rating matrix is [0 4 1 3 2]
  • the Euclidean distance between the similar user group rating matrix [0 4 1 3 2] and the target user ’s rating row vector [1 3 1 5 2] is Euclidean distance vector of similar user groups Multiplied by the target user's line vector [1 3 1 5 2] That is, a product recommendation row vector composed of a comprehensive score value of each product by a similar user group is obtained.
  • the product recommendation row vector is calculated according to the rating row vector corresponding to each user in the cluster group to which the target user belongs and the target user's rating row vector, the ranking of each product in the product recommendation row vector
  • the top products can be used as one of the components of the product recommendation list. In this way, the recommended products obtained through the product recommendation row vector fully consider the preferences of the approximate users, so they can accurately reflect the preferences of the target users.
  • a product recommendation list is obtained from the product corresponding to the comprehensive rating value whose rating rank is before the preset second ranking threshold in the product recommendation row vector, and the product recommendation list is pushed to the receiving end corresponding to the target user.
  • the comprehensive score of each product in the cluster group to which the target user belongs can be known, and the ranking of the comprehensive score is before the second ranking threshold (For example, if the second ranking threshold is set to 4) the product corresponding to the score can be used as a recommended item in the product recommendation list.
  • the top three products in the product recommendation row vector corresponding to the similar user (user 2) of the target user (user 1) are product 2, product 4, and product 5, respectively.
  • the above three products are used as the product list Push to the target user.
  • the product preferences of similar users can be used as the main factors considered when recommending products to target users, and product recommendations can be made more reasonably.
  • the method before step S110, the method further includes:
  • the DBSCAN clustering model is used to cluster the statistical vectors corresponding to the commodity keyword set to obtain at least one commodity clustering cluster;
  • a weighted average is obtained according to the score corresponding to each product name in the similar product result to obtain a product weighted score corresponding to the blank value, so as to update the blank value to the corresponding product weighted score.
  • the user can select one or more of a variety of commodities and make a purchase.
  • a set of historical commodity information is stored.
  • Each historical commodity information in the historical commodity information set includes a commodity name and a commodity attribute, where the attribute of the commodity includes the price, label, brand, and function of the commodity.
  • each historical commodity can be The information is simply expressed as a corresponding set of product keywords.
  • the word frequency-inverse text frequency index model is a commonly used weighting technique for information retrieval and data mining.
  • TF means term frequency (Term Frequency)
  • IDF means inverse text frequency index (Inverse Document Frequency).
  • TF-IDF is a statistical method used to evaluate the importance of a word to one of the documents in a document set or a corpus. The importance of a word increases proportionally with the number of times it appears in the document, but at the same time it decreases inversely with the frequency of its appearance in the corpus.
  • the key of the commodity can be learned through the Word2Vec model (Word2Vec is a model for learning semantic knowledge in an unsupervised manner from a large number of text corpora)
  • the word set is converted into a word vector corresponding to each historical commodity information.
  • the basketball, Spalding, and XX models each correspond to a vector. Only one value in the vector is 1, and the rest are all 0. Enter the vector corresponding to the above information into the Word2Vec model to convert it into low-dimensional continuous values. This is a dense vector, and words with similar meanings will be mapped to similar positions in the vector space.
  • the average of the word vectors of each product keyword may be taken as the statistical vector of the product.
  • each historical commodity information in the historical commodity information set is converted into a corresponding statistical vector, and then the statistical vector corresponding to the commodity keyword set is clustered by the DBSCAN clustering model to obtain at least one commodity cluster cluster.
  • the row vector to which the blank value belongs in the initial user-score matrix it can be known which user ’s product score for which product is a blank value, and at this time, the product corresponding to the blank value is first obtained You can know the statistical vector corresponding to the product name by name. Then, judging the product cluster to which the statistical vector belongs, the similar product names of other products in the product cluster can be obtained as the similar product results of the product names corresponding to the blank values. Since the blank value is in the row vector to which the initial user-score matrix belongs, the user's score for each similar product name in the similar product result can be obtained. Finally, the weighted average of the user's ratings for each similar product name is obtained to obtain the product weighted score corresponding to the blank value.
  • the weighted average is obtained according to the score corresponding to each product name in the similar product result, and the product weighted score corresponding to the blank value is obtained, including:
  • Score k represents the product weighted score of the blank corresponding to the product k
  • m is the total number of similar products c in the similar product results.
  • the score of user 1 for product 2 is a blank value
  • the results of similar products corresponding to product 2 are product 4 and product 5
  • the scores of user 1 for product 4 and product 5 are 3 and 4, respectively
  • product 4 corresponds to
  • the distance between the statistical vector and the statistical vector corresponding to commodity 2 is 0.5
  • the distance between the statistical vector corresponding to commodity 5 and the statistical vector corresponding to commodity 2 is 1, then:
  • the Score2 calculated above is used as the product weighted score corresponding to the blank value.
  • the user-scoring matrix can be effectively completed, avoiding the problem of cold start during the recommendation process.
  • the method realizes that the user-scoring matrix is divided into multiple sub-matrices for maintenance, which reduces maintenance costs, and can accurately push the target user's commodity information according to the sub-matrix.
  • An embodiment of the present application further provides a re-clustering-based push device, and the re-clustering-based push device is used to perform any embodiment of the foregoing re-clustering-based push method.
  • FIG. 4 is a schematic block diagram of a re-clustering-based push device provided by an embodiment of the present application.
  • the push device 100 based on re-clustering may be configured in the management server.
  • the re-clustering-based pushing device 100 includes a user clustering unit 110, a clustering judgment unit 120, a similar user rating matrix obtaining unit 130, a product recommendation row vector obtaining unit 140, and an information pushing unit 150.
  • the user clustering unit 110 is configured to cluster the obtained user-scoring matrix by DBSCAN clustering to obtain at least one cluster group and a sub-user-scoring matrix corresponding to each cluster group in one-to-one relationship.
  • the user clustering unit 110 includes:
  • the initial center obtaining unit 111 is configured to use any row vector in the user-scoring matrix as an initial clustering center;
  • the initial cluster group obtaining unit 112 is configured to obtain a line vector with a distance from the center of the initial cluster within a preset scan radius according to a preset minimum number of included points, as an initial cluster group;
  • the cluster group adjustment unit 113 is used to take each row vector in the initial cluster group as a cluster center, and obtain a row vector in the user-scoring matrix directly attainable in density, reachable in density, or connected to the cluster center, as The adjusted cluster group.
  • the cluster judgment unit 120 is configured to obtain the cluster group corresponding to the row vector of the target user according to the target user corresponding to the row vector selected in the sub-user-score matrix.
  • the similar user rating matrix obtaining unit 130 is used to calculate and obtain the Euclidean distance between each rating row vector and the target user's row vector in the cluster group corresponding to the target user, and obtain the ranking first in each Euclidean distance
  • the scoring row vector corresponding to the Euclidean distance before the ranking threshold constitutes a scoring matrix for similar user groups.
  • the product recommendation row vector acquisition unit 140 is configured to acquire a comprehensive rating value of each product of the similar user group according to each rating row vector in the similar user group rating matrix to form a product recommendation row vector.
  • the commodity recommendation row vector acquisition unit 140 includes:
  • the Euclidean distance row vector acquisition unit 141 is used to form the Euclidean distance row vector of the similar user group according to the Euclidean distance between each rating row vector in the similar user group rating matrix and the target user's row vector;
  • the comprehensive score calculation unit 142 is used to multiply the similar user group Euclidean distance row vector and the similar user group score matrix to obtain the comprehensive score value of the similar user group for each product to form a product recommendation row vector.
  • the information pushing unit 150 is used to obtain a product recommendation list from the products corresponding to the comprehensive rating value in the product recommendation row vector whose rating rank is before the preset second ranking threshold, and push the product recommendation list to the corresponding reception of the target user end.
  • the re-clustering-based pushing device 100 further includes:
  • the historical keyword set acquisition unit is used to acquire a historical commodity information set, and extract keyword information from each historical commodity information in the historical commodity information set through the word frequency-inverse text frequency index model to obtain information related to each historical commodity Corresponding product keyword set;
  • the word vector conversion unit is used to obtain the word vector corresponding to each commodity keyword in each commodity keyword set through the Word2Vec model;
  • the statistical vector acquisition unit is used to acquire the average value of the word vector corresponding to each commodity keyword in each commodity keyword set to obtain the statistical vector corresponding to each commodity keyword set;
  • the commodity clustering unit is used to cluster the statistical vectors corresponding to the commodity keyword set by the DBSCAN clustering model to obtain at least one commodity clustering cluster;
  • a vector-to-be-predicted unit for obtaining a statistical vector corresponding to the product name according to the product name corresponding to the blank value if the user-scoring matrix includes a blank value
  • Commodity cluster attribution judgment unit used to obtain the commodity cluster to which the statistical vector corresponding to the commodity name belongs
  • a similar product result obtaining unit configured to obtain a similar product name corresponding to the product name corresponding to the blank value based on the product cluster to which the statistical vector corresponding to the product name belongs, as a similar product result;
  • a similar commodity score obtaining unit configured to obtain a score corresponding to each commodity name in the similar commodity result according to the row vector corresponding to the blank value;
  • the product weighted score acquisition unit is configured to perform weighted averaging based on the score corresponding to each product name in the similar product result to obtain the product weighted score corresponding to the blank value to update the blank value to the corresponding product weighted score.
  • the product weighted score acquisition unit includes:
  • the vector distance set acquisition unit is configured to use the statistical vector corresponding to each product name in the similar product result as a statistical vector group, and use the statistical vector corresponding to the product name corresponding to the blank value as the product score vector to be predicted to obtain the The distance between each statistical vector in the statistical vector group and the score vector of the product to be predicted to obtain a vector distance set;
  • a product weighted total score acquisition unit configured to multiply the corresponding score of each product name in the similar product result by the corresponding vector distance in the vector distance set and sum to obtain a product weighted total score
  • the average score obtaining unit is used to divide the weighted total score of the product by the sum of the vector distances in the vector distance set to obtain the product weighted score corresponding to the blank value.
  • FIG. 7 is a schematic block diagram of a computer device provided by an embodiment of the present application.
  • the computer device 500 includes a processor 502, a memory, and a network interface 505 connected through a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
  • the non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032.
  • the computer program 5032 When executed, it may cause the processor 502 to execute a push method based on re-clustering.
  • the processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.
  • the memory 504 provides an environment for running the computer program 5032 in the non-volatile storage medium 503.
  • the processor 502 can cause the processor 502 to execute a push method based on re-clustering.
  • the network interface 505 is used for network communication, such as the transmission of data information.
  • the processor 502 is used to run the computer program 5032 stored in the memory, so as to implement the re-clustering-based push method of the embodiment of the present application.
  • the embodiment of the computer device shown in FIG. 7 does not constitute a limitation on the specific configuration of the computer device.
  • the computer device may include more or less components than shown in the figure. Or combine certain components, or arrange different components.
  • the computer device may only include a memory and a processor. In such an embodiment, the structures and functions of the memory and the processor are consistent with the embodiment shown in FIG. 7 and will not be repeated here.
  • the processor 502 may be a central processing unit (Central Processing Unit, CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), Application specific integrated circuit (Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may be any conventional processor.
  • a computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, where when the computer program is executed by a processor, the push method based on re-clustering of the embodiment of the present application is implemented.
  • the storage medium may be an internal storage unit of the aforementioned device, such as a hard disk or a memory of the device.
  • the storage medium may also be an external storage device of the device, such as a plug-in hard disk equipped on the device, a smart memory card (Smart) Card (SMC), a secure digital (SD) card, or a flash memory card (Flash Card) etc.
  • the storage medium may also include both an internal storage unit of the device and an external storage device.

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Abstract

La présente invention concerne un appareil et un procédé basés sur la répétition de la répartition, un dispositif informatique (500) et un support d'enregistrement. Le procédé comprend les étapes suivantes : en effectuant un regroupement (clustering) DBSCAN sur des vecteurs de rangée dans une matrice de notation d'utilisateur, obtenir des groupes de cluster classés selon des utilisateurs et une matrice de notation de sous-utilisateur ayant une correspondance biunivoque à chaque groupe de clusters ; obtenir un vecteur de rangée sélectionné dans la matrice de notation de sous-utilisateur en tant qu'utilisateur cible ; en fonction de la matrice de notation de sous-utilisateur où se trouve l'utilisateur cible, obtenir une matrice de notation de groupe d'utilisateurs similaire de l'utilisateur cible et un vecteur de rangée de recommandation de produits correspondant à la matrice de notation de groupe d'utilisateurs similaire ; et obtenir une liste de recommandation de produits selon le vecteur de rangée de recommandation de produits, et pousser la liste de recommandations de produits vers une extrémité de réception correspondant à l'utilisateur cible.
PCT/CN2018/125334 2018-10-12 2018-12-29 Procédé et appareil de poussée basés sur le re-clustering, dispositif informatique et support d'enregistrement WO2020073534A1 (fr)

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