CN108460619B - Method for providing collaborative recommendation model fusing explicit and implicit feedback - Google Patents

Method for providing collaborative recommendation model fusing explicit and implicit feedback Download PDF

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CN108460619B
CN108460619B CN201810038717.3A CN201810038717A CN108460619B CN 108460619 B CN108460619 B CN 108460619B CN 201810038717 A CN201810038717 A CN 201810038717A CN 108460619 B CN108460619 B CN 108460619B
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汤景凡
龚泽鑫
张旻
姜明
杜炼
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Abstract

The invention discloses a collaborative recommendation model integrating explicit-implicit feedback, which is used for completing the recommendation tasks of a user in two steps from the perspective of simulating the real shopping process of the user, firstly, analyzing implicit feedback data of the user through a ranking-oriented recommendation model to model, then, selecting the most likely browsed articles of the user from an article set to be ranked, then, learning a user characteristic matrix and an article characteristic matrix through explicit feedback data of the user by adopting a ranking-oriented recommendation model for predicting the ranking, then, rearranging the sequence of the previously selected article sets based on the ranking, and returning the ranked articles to a target user as a final recommendation list, thereby greatly improving the recommendation accuracy and having wide commercial application value.

Description

Method for providing collaborative recommendation model fusing explicit and implicit feedback
Technical Field
The invention belongs to the technical field of computer software, and particularly relates to a collaborative recommendation model integrating explicit-implicit feedback.
Background
In most current online shopping platforms, the historical behavior data (such as rating, praise, browsing history) of the user implicitly contains the preference information of the user, and many recommendation systems use the preference information as one of important sources of input data. According to the feedback mechanism of the user, the online behavior data of the user can be divided into explicit feedback data and implicit feedback data, and the two data are concurrent with the current e-commerce field, and the explicit feedback data or the implicit feedback data rarely independently exist. However, most of the prior personalized recommendation technologies focus on the single explicit feedback or implicit feedback, which is difficult to meet the urgent needs of the current e-commerce for recommendation technologies, and how to fuse the explicit and implicit feedback for recommendation tasks is one of the difficulties of research. In addition, the sequence in the finally generated recommended item set influences the purchasing intention of people, and it is generally considered that the former item is the most desirable item to purchase by the user, so the sequence between different items in the recommendation result should be considered by the recommendation task.
In the present invention, we consider the purchase flow of the user in the real scene as follows: the user browses the commodities for a long time and selects the commodities which the user wants to buy. The user leaves much implicit feedback data (such as clicks, browsing, purchasing records and the like) in the browsing process, and the evaluation information after purchasing is explicit feedback data (such as scores, ratings and the like) of the user, so the final recommendation result should not depend on the explicit feedback data or the implicit feedback data alone, and the recommendation task can be divided into two steps: the method comprises the steps of firstly generating an item set which is most likely to be browsed by a user, and then reordering the obtained item set to generate an item set which is most likely to be purchased by the user.
Disclosure of Invention
The invention aims to provide a collaborative recommendation model fusing explicit-implicit feedback. The model has good recommendation effect and can realize personalized recommendation.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1, training a required ranking-oriented recommendation model by using implicit feedback data, and selecting an article set I which is most likely to be browsed by a user;
step 2, adding explicit feedback data and implicit feedback data into the scoring-oriented model to train the model, and learning a user characteristic matrix and an article characteristic matrix for predicting scoring; reordering the item set I obtained in the step 1 to obtain an item set II which is most probably purchased by the user, and returning the item set II to the target user as a final recommendation result;
through the steps, the implicit feedback data and the explicit feedback data of the user are used for personalized recommendation, and the purpose of improving the personalized recommendation effect is achieved.
The step 1 is specifically realized as follows:
the recommendation algorithm used by the ranking-oriented recommendation model in step 1 may adopt a more excellent algorithm bpr (bayesian qualified ranking) applied to implicit feedback data in the current ranking learning algorithm, and the algorithm is implemented as follows:
firstly, carrying out data pair preprocessing on historical scoring data of a user: the BPR algorithm processes the user's score data for an item (the item with which the user has interacted is denoted as "1", and the item without interaction is denoted as "0") into a set of pair < i, j >, where i is the item with a score of 1 and j is the item with a score of 0. Expressed in a triplet < u, i, j >: user "u" likes item "i" more than item "j".
Preconditions of the recommendation model:
① the preference behavior between users is independent.
② the order bias relationship of the same user to different items is independent.
Thus, a target is defined that needs maximization:
Πp(Θ|i＞uj)∝Πp(i＞uj|Θ)p(Θ) (1)
wherein Θ is the recommended model parameter that is sought, including: a user's feature matrix U and an item's feature matrix V. p (Θ | i >)uj) Representing the posterior probability, p (i >)uj | Θ) represents the likelihood part, p (Θ) represents the prior probability, i >uj indicates that user "u" likes item "i" more than item "j".
Wherein with respect to the likelihood part: p (i >)uj|Θ)＝δ(xu，i-xu，j)，
Figure GDA0002320060200000021
xu，i＝pu·piLet the prior probability obey the following distribution: theta to N (0, lambda)ΘI) Then the density function of the prior probability is:
Figure GDA0002320060200000022
based on the above assumptions, the optimization objective is further expanded to obtain:
Figure GDA0002320060200000031
wherein xu，i<pu，qi>The preference degree of the user u for the item i is expressed in the form of an inner product of two vectors, and theta is a parameter of the recommendation model. In order to maximize the function value of the above expression (3)And solved by a stochastic gradient descent algorithm (SGD). The invention can obtain the item set I which is most likely to be browsed by the user by the method.
The step 2 is realized as follows:
for the recommendation model facing the score in step 2, the SVD + + model may be used to complete the screening of the item set ii most likely to be purchased by the user. The SVD + + model is improved by integrating implicit feedback data on the basis of a Singular Value Decomposition (SVD) model. The core of SVD is a matrix decomposition technology, and the basic idea is to extract some features according to a user's historical behavior data set (such as user's score data) as the basis of a recommendation task. For movie recommendations, these features can be understood as the degree of fun, the degree of horror, the degree of adventure, etc. of the movie.
In the SVD model, each user has an item that is not scored for which the user may actually have a potential interest, but the SVD incorrectly believes that the user is not interested in the item. The SVD + + model incorporates the fact that the user is potentially interested.
Figure GDA0002320060200000032
Where N (u) represents the user u scoring item set, and W is called the potential item feature matrix. The objective function is defined as follows:
Figure GDA0002320060200000033
wherein the content of the first and second substances,
Figure GDA0002320060200000034
vector biRepresenting the deviation of the score of movie i from the mean score, vector buRepresenting the deviation of the score made by user u from the average score, the average score is scored as μ.
The invention has the following beneficial effects:
the invention discloses a collaborative recommendation model integrating explicit-implicit feedback. In the traditional recommendation technology, the rating-oriented recommendation model only utilizes the explicit feedback data of the users, so that the capability of capturing the like similarity between the users is lost, and the ranking-oriented recommendation model only considers whether the preferences of the users to the product pairs are consistent and ignores the important information of the preference degree between the users. The invention analyzes and researches two traditional recommendation models and finds respective defects. Therefore, from the perspective of simulating the real shopping process of the user, the invention combines the advantages of the two recommendation models together by two steps to complete the recommendation task, firstly, the recommendation model facing the sequencing analyzes the implicit feedback data of the user to model, then, the most likely browsed items of the user are selected from the item set to be sequenced, then, the recommendation model facing the grading learns the user characteristic matrix and the item characteristic matrix for predicting the grading through the explicit feedback data of the user, and then, the selected item set is rearranged in the sequence based on the grading and is returned to the target user as the final recommendation list.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
A collaborative recommendation model fusing explicit and implicit feedback is shown in the overall framework of fig. 1. The method comprises the following steps:
step 1, training a required ranking-oriented recommendation model (such as BPR) by using implicit feedback data, and selecting an item set I which is most likely to be browsed by a user.
And 2, adding explicit feedback data and implicit feedback data into a rating-oriented model (such as SVD + +) to train the model, reordering the item set I obtained in the step 1 to obtain an item set II which is most likely to be purchased by a user, and taking the item set II as a final recommendation result.
Through the steps, the implicit feedback data and the explicit feedback data of the user are used for personalized recommendation, and the purpose of improving the personalized recommendation effect is achieved.
The step 1 is specifically realized as follows:
the recommendation algorithm used by the ranking-oriented recommendation model in step 1 may adopt a more excellent algorithm bpr (bayesian qualified ranking) applied to implicit feedback data in the current ranking learning algorithm, and the algorithm is implemented as follows:
firstly, carrying out data pair preprocessing on historical scoring data of a user: the BPR algorithm processes the user's score data for an item (the item with which the user has interacted is denoted as "1", and the item without interaction is denoted as "0") into a set of pair < i, j >, where i is the item with a score of 1 and j is the item with a score of 0. Expressed in a triplet < u, i, j >: user "u" likes item "i" more than item "j".
Preconditions of the recommendation model:
① the preference behavior between users is independent.
② the order bias relationship of the same user to different items is independent.
Thus, a target is defined that needs maximization:
Πp(Θ|i＞uj)∝Πp(i＞uj|Θ)p(Θ) (1)
wherein Θ is the recommended model parameter that is sought, including: a user's feature matrix U and an item's feature matrix V. p (Θ | i >)uj) Representing the posterior probability, p (i >)uj | Θ) represents the likelihood part, p (Θ) represents the prior probability, i >uj indicates that user "u" likes item "i" more than item "j".
Wherein with respect to the likelihood part: p (i >)uj|Θ)＝δ(xu，i-xu，j)，
Figure GDA0002320060200000051
xu，i＝pu·piLet the prior probability obey the following distribution: theta to N (0, lambda)ΘI) Then the density function of the prior probability is:
Figure GDA0002320060200000052
based on the above assumptions, the optimization objective is further expanded to obtain:
Figure GDA0002320060200000053
Figure GDA0002320060200000061
wherein xu，i<pu，qi>The preference degree of the user u for the item i is expressed in the form of an inner product of two vectors, and theta is a parameter of the recommendation model. In order to maximize the function value of the above expression (3), it is solved by a random gradient descent algorithm (SGD). The invention can obtain the item set I which is most likely to be browsed by the user by the method.
The step 2 is realized as follows:
for the recommendation model facing the score in step 2, the SVD + + model may be used to complete the screening of the item set ii most likely to be purchased by the user. The SVD + + model is improved by integrating implicit feedback data on the basis of a Singular Value Decomposition (SVD) model. The core of SVD is a matrix decomposition technology, and the basic idea is to extract some features according to a user's historical behavior data set (such as user's score data) as the basis of a recommendation task. For movie recommendations, these features can be understood as the degree of fun, the degree of horror, the degree of adventure, etc. of the movie.
In the SVD model, each user has an item that is not scored for which the user may actually have a potential interest, but the SVD incorrectly believes that the user is not interested in the item. The SVD + + model incorporates the fact that the user is potentially interested.
Figure GDA0002320060200000062
Where N (u) represents the user u scoring item set, and W is called the potential item feature matrix. The objective function is defined as follows:
Figure GDA0002320060200000063
wherein the content of the first and second substances,
Figure GDA0002320060200000064
vector biRepresenting the deviation of the score of movie i from the mean score, vector buRepresenting the deviation of the score made by user u from the average score, the average score is scored as μ.

Claims (1)

1. A method for providing a collaborative recommendation model integrating explicit-implicit feedback is characterized in that from the perspective of simulating the real shopping process of a user, the method comprises the following specific steps:
step 1, training a required ranking-oriented recommendation model by using implicit feedback data, and selecting an article set I which is most likely to be browsed by a user;
step 2, adding explicit feedback data and implicit feedback data into the scoring-oriented model to train the model, and learning a user characteristic matrix and an article characteristic matrix for predicting scoring; reordering the item set I obtained in the step 1 to obtain an item set II which is most probably purchased by the user, and returning the item set II to the target user as a final recommendation result;
the recommendation algorithm used by the ranking-oriented recommendation model in the step 1 adopts a BPR algorithm applied to implicit feedback data in the current ranking learning algorithm, and is specifically realized as follows:
firstly, carrying out data pair preprocessing on historical scoring data of a user: the BPR algorithm processes the scoring data of the user to the articles into a set < i, j > of pair, wherein i is the article with the score of 1, and j is the article with the score of 0; expressed in a triplet < u, i, j >: user "u" likes item "i" more than item "j"; the item with interaction by the user is marked as '1', and the item without interaction is marked as '0';
the preconditions of the ranking-oriented recommendation model are:
① the preference behavior among users is independent;
② the partial order relations of different articles of the same user are independent;
thus, a target is defined that needs maximization:
∏p(Θ|i＞uj)∝∏p(i＞uj|Θ)p(Θ) (1)
wherein Θ is the recommended model parameter that is sought, including: a characteristic matrix U of the user and a characteristic matrix V of the article; p (Θ | i >)uj) Representing the posterior probability, p (i >)uj | Θ) represents the likelihood part, p (Θ) represents the prior probability, i >uj indicates that user "u" likes item "i" more than item "j";
wherein with respect to the likelihood part: p (i >)uj|Θ)＝δ(xu，i-xu，j)，
Figure FDA0002320060190000011
xu，i＝pu·piLet the prior probability obey the following distribution: theta to N (0, lambda)ΘI) Then the density function of the prior probability is:
Figure FDA0002320060190000021
based on the above assumptions, the optimization objective is further expanded to obtain:
Πp(i＞uj|Θ)p(Θ)∝ln(p(i＞uj|Θ)p(Θ))＝∑(ln δ(xu，i-xu，j)+ln p(Θ))
＝∑(lnδ(xu，i-xu，j)-λΘ||Θ||2)
＝∑(lnδ(xu，i-xu，j)-λΘ||pu||2-λΘ||qi||2-λΘ||qj||2)
＝∑(lnδ(pu·qi-pu·qj)-λe||pu||2-λΘ||qi||2-λΘ||qj||2) (3)
wherein xu，i<pu，qi>Representing the preference degree of the user u to the item i in the form of the inner product of two vectors, wherein theta is a recommendation model parameter; in order to maximize the function value of the expression (3), solving by a random gradient descent algorithm to obtain an item set I which is most likely to be browsed by a user;
in the step 2, aiming at the scoring recommendation model, screening the item set II most probably purchased by the user by using an SVD + + model, wherein the fact that the user has potential interest is blended into the SVD + + model;
Figure FDA0002320060190000022
wherein N (u) represents a user u scoring item set, and W is called a potential item feature matrix; the objective function is defined as follows:
Figure FDA0002320060190000023
wherein the content of the first and second substances,
Figure FDA0002320060190000024
vector biRepresenting the deviation of the score of the item i to be recommended from the mean score, vector buRepresenting the deviation of the score made by user u from the average score, the average score is scored as μ.
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