CN110209946B - Social and community-based product recommendation method, system and storage medium - Google Patents

Social and community-based product recommendation method, system and storage medium Download PDF

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CN110209946B
CN110209946B CN201910496478.0A CN201910496478A CN110209946B CN 110209946 B CN110209946 B CN 110209946B CN 201910496478 A CN201910496478 A CN 201910496478A CN 110209946 B CN110209946 B CN 110209946B
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孙见山
应蓉蓉
刘业政
姜元春
凌海峰
孙春华
陈夏雨
刘春丽
耿杰
宋建
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Abstract

The invention provides a social contact and community-based product recommendation method, a social contact and community-based product recommendation system and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring interaction data, social relationship data and community relationship data of a user and a product, and forming historical data; determining a feedback relationship between the user and the product based on the historical data and a preset partial order relationship; determining a preference relationship of the user to the product based on the historical data and the feedback relationship; determining an objective function of the preference relationship; acquiring a preference score of a user for a product based on the target function; and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result. The invention can accurately recommend the product to the user.

Description

Social and community-based product recommendation method, system and storage medium
Technical Field
The invention relates to the field of data processing, in particular to a product recommendation method, a product recommendation system and a storage medium based on social contact and community.
Background
With the development of internet technology, research on personalized recommendation based on social networks is more active. And the social recommendation method can be divided into a scoring prediction algorithm based on explicit feedback data and a personalized ranking algorithm based on implicit feedback data. Wherein, the explicit feedback data generally refers to the grade, i.e. the grade of 1-5, can obviously reflect the preference degree of the user for the product. The implicit feedback data refers to the interaction relationship between the user and the product, such as browsing, purchasing, collecting and the like, and the preference degree of the user for the product cannot be obviously reflected. For those products where the user does not interact with the product, it cannot be shown that the user does not like the product, and it may be that the user has not yet discovered the product. Because implicit feedback data widely exists, the acquisition cost is low, and the method is close to reality, the sorting algorithm based on the implicit feedback data gets more and more attention.
The social recommendation method provided by the prior art is mainly based on a Bayesian personalized ranking algorithm to optimize ranking. Products interacted with the user are set as positive feedback, products not interacted with the user are set as negative feedback, the preference of the user on the interacted products is assumed to be larger than that of the non-interacted products, and the social relationship of friends of the user is considered, so that the product recommendation is optimized by means of a Bayesian personalized ranking algorithm.
However, the inventor of the present application finds that, when the recommendation method is actually applied, the social relationships of users in an online social network are relatively sparse, and therefore, the recommendation result of a product cannot be accurately obtained only by considering the social relationships. The prior art recommendation methods are therefore not accurate enough.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a product recommendation method, a product recommendation system and a storage medium based on social contact and community, and solves the technical problem that the prior art cannot accurately recommend products.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention provides a product recommendation method based on social contact and community, which solves the technical problem, the recommendation method is executed by a computer and comprises the following steps:
acquiring interaction data, social relationship data and community relationship data of a user and a product, and forming historical data;
determining a feedback relationship between the user and the product based on the historical data and a preset partial order relationship;
determining a preference relationship of the user to the product based on the historical data and the feedback relationship;
determining an objective function of the preference relationship;
acquiring a preference score of a user for a product based on the target function;
and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result.
Preferably, the historical data includes:
the system comprises a user set U, a product set P, a set D in which users and products have interaction, a social relationship set S and a community relationship set G.
Preferably, the preset partial order relationship is as follows:
positive feedback > social feedback > community feedback > negative feedback;
the feedback relationship between the user and the product comprises:
and (3) positive feedback assembly: PF (particle Filter)u＝{＜u,pi＞}，
Wherein: < u, piIs e.g. D, represents user u and product piInteraction exists;
social feedback set: SFu＝{＜u,ps＞}，
Wherein: < u, psThe social friends of the user u have interaction, but the user u does not have interactive products;
community feedback set: GFu＝{＜u,pg＞}，
Wherein: < u, pgProducts that show that social friends of the user u have interaction, but the user u does not interact with the social friends of the user u and does not interact with the social friends of the user u;
and (3) negative feedback integration: NFu＝{＜u,pj＞}，
Wherein: < u, pjThe expression > represents that the user u does not interact with the social friends and the social group friends of the user u, and the social group friends of the user u also do not have interactive products.
Preferably, the preference relationship of the user to the product is represented by a matrix decomposition model based on the user set U to the product set P:
Figure GDA0002888269470000031
wherein:
Figure GDA0002888269470000032
representing a preference set of the user set U to the product set P;
w represents a feature matrix of the user set U;
h represents a feature matrix of the product set P;
b represents a deviation term of the product set P.
Preferably, the objective function of the preference relationship is as follows:
Figure GDA0002888269470000041
wherein:
Figure GDA0002888269470000042
indicating that user u is facing feedback set PFuProduct p of ChineseiA preference for (c);
Figure GDA0002888269470000043
representing a set SF of social feedback for a user uuProduct p of ChinesesA preference for (c);
Figure GDA0002888269470000044
representing a set GF of user u-to-community feedbackuProduct p of ChinesegA preference for (c);
Figure GDA0002888269470000045
representing user u versus negative feedback set NFuProduct p of ChinesejA preference for (c);
σ (-) represents a logistic function;
Θ denotes a set of parameters in the matrix decomposition model, i.e., Θ ═ { W, H, b };
λΘis a regularization parameter;
CSrepresenting user u and product psThe number of friends who have interacted with the product in the friends of the user u is not interacted;
Cgrepresenting user u and product pgAnd no interaction exists, and the social friends of the user u have no interaction with the product, but the social friends of the user u have the number of the interactive social friends with the product.
Preferably, the obtaining of the preference score of the user for the product based on the objective function specifically includes:
s501, setting the iteration number as iter, wherein iter is 1;
s502, initializing a parameter set at random based on normal distribution:
Θiter＝{Witer,Hiter,biter}
s503, setting a circulation variable to be eta, wherein eta is 1;
s504, traversing an interaction relation set D of the user and the product under the iter iteration;
s505, in the eta cycle process, one user u is randomly selected, and meanwhile, a corresponding positive feedback product p is taken out from a positive feedback set, a social feedback set, a community feedback set and a negative feedback set corresponding to the user uiSocial feedback product psCommunity feedback product pgAnd a negative feedback product pj; thereby obtaining a group of user partial order product combinations of the eta traversal under the iter iteration
Figure GDA0002888269470000051
S506, combining the products in the user partial order
Figure GDA0002888269470000052
Substituting the target function into the target function to obtain the target function of the eta cycle under the iter iteration
Figure GDA0002888269470000053
S507, updating the objective function based on the random gradient rise method
Figure GDA0002888269470000054
Middle parameter
Figure GDA0002888269470000055
And
Figure GDA0002888269470000056
a gradient of (a);
s508, assigning eta +1 to eta, judging whether eta > | D | is true, and if yes, executing the step S509; otherwise, returning to step S505;
s509, judging parameters
Figure GDA0002888269470000057
Whether convergence is achieved, if convergence is achieved, an optimal parameter set is obtained
Figure GDA0002888269470000058
Otherwise, iter +1 is assigned to iter, and the procedure returns to step S503.
Preferably, the obtaining of the product recommendation result specifically includes:
and randomly selecting a user v in the product set U, and sequencing all non-interactive products of the user v in the product set P in a descending order according to the preference score to obtain a product recommendation result.
Preferably, the method further comprises the following steps:
evaluating the product recommendation, the evaluating comprising: precision @ K assessment, Recall @ K assessment, MRR @ K assessment, MAP @ K assessment.
The invention provides a product recommendation system based on social contact and community, which solves the technical problem, and comprises a computer, wherein the computer comprises:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
acquiring interaction data, social relationship data and community relationship data of a user and a product, and forming historical data;
determining a feedback relationship between the user and the product based on the historical data and a preset partial order relationship;
determining a preference relationship of the user to the product based on the historical data and the feedback relationship;
determining an objective function of the preference relationship;
acquiring a preference score of a user for a product based on the target function;
and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result.
The present invention provides a computer readable storage medium, having at least one instruction stored thereon, at least the one instruction being loaded and executed by a processor to implement the method as described above
(III) advantageous effects
The invention provides a social and community-based product recommendation method, a system and a storage medium. Compared with the prior art, the method has the following beneficial effects:
according to the method, historical data is formed by acquiring interaction data, social relationship data and community relationship data of a user and a product; determining a feedback relationship between a user and a product based on historical data and a preset partial order relationship; determining a preference relationship of the user to the product based on the historical data and the feedback relationship; determining an objective function of the preference relationship; acquiring a preference score of a user for a product based on a target function; and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result. According to the method and the system, the interaction between the user and the product is analyzed by combining the social relationship and the community relationship of the user, and the preference of the user is divided in a finer granularity, so that the recommendation result is more accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is an overall flowchart of a social and community-based product recommendation method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application solves the problem that products cannot be accurately recommended in the prior art by providing a product recommendation method, a product recommendation system and a storage medium based on social contact and community, and realizes accurate recommendation of products.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
according to the method and the device, historical data are formed by acquiring the interaction data, social relation data and community relation data of the user and the product; determining a feedback relationship between a user and a product based on historical data and a preset partial order relationship; determining a preference relationship of the user to the product based on the historical data and the feedback relationship; determining an objective function of the preference relationship; acquiring a preference score of a user for a product based on a target function; and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result. According to the embodiment of the invention, the interaction between the user and the product is analyzed by combining the social relationship and the community relationship of the user, and the preference of the user is divided in a finer granularity, so that the recommendation result is more accurate.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
An embodiment of the present invention provides a social and community-based product recommendation method, as shown in fig. 1, where the method is executed by a computer and includes the following steps:
s1, acquiring interaction data, social relationship data and community relationship data of the user and the product, and forming historical data;
s2, determining the feedback relationship between the user and the product based on the historical data and the preset partial order relationship;
s3, determining the preference relationship of the user to the product based on the historical data and the feedback relationship;
s4, determining an objective function of the preference relationship;
s5, obtaining the preference score of the user for the product based on the target function;
and S6, sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result.
According to the method and the device, historical data are formed by acquiring the interaction data, social relation data and community relation data of the user and the product; determining a feedback relationship between a user and a product based on historical data and a preset partial order relationship; determining a preference relationship of the user to the product based on the historical data and the feedback relationship; determining an objective function of the preference relationship; acquiring a preference score of a user for a product based on a target function; and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result. According to the embodiment of the invention, the interaction between the user and the product is analyzed by combining the social relationship and the community relationship of the user, and the preference of the user is divided in a finer granularity, so that the recommendation result is more accurate.
The following steps are described in detail:
in step S1, interaction data, social relationship data, and community relationship data of the user and the product are acquired, and history data is formed.
Specifically, the embodiment of the invention can obtain the following data information based on an internet platform:
the system comprises a user set U, a product set P, a set D in which users and products have interaction, a social relationship set S and a community relationship set G.
Setting a binary group < u, pi"E D denotes user u and product piThere is over-interaction. Wherein U is E.U, piAnd e.g. P, the total users | U | ═ M, and the products | P | ═ N.
Binary < ui,uj"E S means user uiAnd user ujAre friends; binary < ui,uj"E G denotes user uiAnd user ujJoin the same community.
In step S2, a feedback relationship between the user and the product is determined based on the history data and a pre-set partial order relationship.
Specifically, the feedback relationship is represented by a positive feedback set, a social feedback set, a community feedback set, and a negative feedback set.
In practical application, the social relationship is a two-way connection between friends, and belongs to a strong connection. The community relation is added by the user according to the interest of the user and belongs to one-way connection. One user may join multiple communities, with multiple users in a community. And the users have a plurality of interests, so that the connection between every two users belonging to the same community belongs to weak connection. Therefore, the preset feedback set in the embodiment of the present invention has the following partial order relationship:
positive feedback > social feedback > community feedback > negative feedback.
Based on the above setting, the feedback relationship can be obtained as follows:
the positive feedback is integrated into PFu＝{＜u,pi＞}。
Wherein: < u, pi＞∈D。
Social feedback set is SFu＝{＜u,ps＞}。
Wherein: < u, psIs a product that user u's social friends have interacted with, but user u itself has not interacted with.
Community feedback set is GFu＝{＜u,pg＞}。
Wherein: < u, pgIs a product that the social friends of user u have interacted, but user u itself has no interaction and the social friends of user u also have no interaction.
Negative feedback integration into NFu＝{＜u,pj＞}。
Wherein: < u, pjIs a product that user u itself has no interaction and that user u's social and social friends have no interaction.
The above set simultaneously satisfies the following conditions:
Figure GDA0002888269470000111
PFu∪SFu∪GFu∪NFu＝P
that is, the four feedback sets include all of the product data, and no product data exists in any two feedback sets at the same time.
In step S3, a preference relationship of the user for the product is determined based on the history data and the feedback relationship.
Specifically, a preference relationship is represented based on a matrix decomposition model of the user set U to the product set P, and the preference relationship specifically includes:
Figure GDA0002888269470000112
wherein:
Figure GDA0002888269470000113
representing a preference set of the user set U to the product set P;
w represents a feature matrix of the user set U;
h represents a feature matrix of the product set P;
b represents a deviation term of the product set P.
The dimension of W is M x K dimensions and the dimension of H is N x K dimensions.
Based on the feedback set corresponding to the preference relationship, the following results are obtained:
Figure GDA0002888269470000114
Figure GDA0002888269470000115
Figure GDA0002888269470000116
Figure GDA0002888269470000117
wherein:
Figure GDA0002888269470000118
indicating that user u is facing feedback set PFuProduct p of ChineseiA preference for (c);
Figure GDA0002888269470000119
representing a set SF of social feedback for a user uuProduct p of ChinesesA preference for (c);
Figure GDA0002888269470000121
representing a set GF of user u-to-community feedbackuProduct p of ChinesegA preference for (c);
Figure GDA0002888269470000122
representing user u versus negative feedback set NFuProduct p of ChinesejPreference (c) of (c).
In step S4, an objective function of the preference relationship is determined.
Specifically, the objective function is constructed based on a Bayesian personalized sorting method. And taking the matrix decomposition model as a measure of the partial order relation. And performing optimization solution by using a gradient ascent algorithm to obtain each parameter value in the matrix decomposition model.
The objective function is specifically:
Figure GDA0002888269470000123
wherein:
Figure GDA0002888269470000124
indicating that user u is facing feedback set PFuProduct p of ChineseiA preference for (c);
Figure GDA0002888269470000125
representing a set SF of social feedback for a user uuProduct p of ChinesesA preference for (c);
Figure GDA0002888269470000126
representing a set GF of user u-to-community feedbackuProduct p of ChinesegA preference for (c);
Figure GDA0002888269470000127
representing user u versus negative feedback set NFuProduct p of ChinesejA preference for (c);
σ (-) represents a logistic function;
Θ denotes a set of parameters in the matrix decomposition model, i.e., Θ ═ { W, H, b };
λΘfor regularizingCounting;
CSrepresenting user u and product psThe number of friends who have interacted with the product in the friends of the user u is not interacted;
Cgrepresenting user u and product pgAnd no interaction exists, and the social friends of the user u have no interaction with the product, but the social friends of the user u have the number of the interactive social friends with the product.
Wherein, CSThe larger the value, the user u is represented to the product piWith product psThe closer the preference of (a); cgThe larger the value, the more the user is indicated to product psWith product pgThe closer the preference difference of (a) is.
The objective function is to train the preference score of the user on the product to meet the order bias relationship proposed by the invention.
In step S5, a preference score of the user for the product is obtained based on the objective function.
Specifically, the method comprises the following steps:
s501, setting the iteration number as iter, wherein iter is 1;
s502, initializing a parameter set at random based on normal distribution:
Θiter＝{Witer,Hiter,biter}
s503, setting a circulation variable to be eta, wherein eta is 1;
s504, traversing an interaction relation set D of the user and the product under iter iteration, and randomly sampling the partial sequence relation for | D | times;
s505, in the eta cycle process, randomly selecting a user u, and meanwhile, selecting a positive feedback set PF corresponding to the user uuRandomly selecting a positive feedback product piFrom the social feedback set SF corresponding to the user uuRandomly selecting a social feedback product psFrom the community feedback set GF corresponding to the user uuRandomly selecting a community feedback product pgNF in negative feedback set corresponding to the user uuRandomly selecting a negative feedback product pj(ii) a Thereby obtainingObtaining a group of user partial order product combinations of the eta traversal under the iter iteration
Figure GDA0002888269470000131
S506, combining the products in the user partial order
Figure GDA0002888269470000132
Substituting the target function into the target function to obtain the target function of the eta cycle under the iter iteration
Figure GDA0002888269470000133
S507, updating the objective function based on the random gradient rise method
Figure GDA0002888269470000134
Middle parameter
Figure GDA0002888269470000135
And
Figure GDA0002888269470000136
a gradient of (a);
s508, assigning eta +1 to eta, judging whether eta > | D | is true, and if yes, executing the step S509; otherwise, returning to step S505;
s509, judging parameters
Figure GDA0002888269470000141
Whether convergence is achieved, if convergence is achieved, an optimal parameter set is obtained
Figure GDA0002888269470000142
Otherwise, iter +1 is assigned to iter, and the procedure returns to step S503.
In step S6, the non-interactive products of the user are sorted based on the preference scores, and a product recommendation result is obtained.
Specifically, one user v in the product set U is randomly selected, all non-interactive products of the user v in the product set P are sorted in a descending order according to the preference score, and the method can be preset to extract the first n products to form a product recommendation list, so as to obtain a product recommendation result and recommend the product recommendation result to the user.
According to the method and the device, on the basis of interaction between the user and the product, the social information and the community information are considered, the preference sequence of the user to the non-interacted product is constructed, and therefore the user preference is divided in a finer granularity mode.
The embodiment of the invention also comprises the following steps:
and S7, evaluating the product recommendation result.
Specifically, the embodiment of the invention evaluates the recommendation result by using Precision @ K and Recall @ K, MRR @ K, MAP @ K.
First, the data set is divided into a training set and a test set. The invention uses a training set to train a model to obtain the preference score x of a user on a product. And obtaining a recommendation list of each user according to the preference scores, and comparing and evaluating the recommendation list obtained by the training set and the test set to obtain the quality of the recommendation result.
Wherein precision @ K represents the proportion of items that the user likes in the recommendation list. The recommendation accuracy for a single user u is:
Figure GDA0002888269470000151
wherein:
hit represents how many of the user u's recommendation list are actually purchased by the user u;
and K is the length of the recommended result.
The overall accuracy is:
Figure GDA0002888269470000152
i.e. the prediction accuracy of the population sample is the average of the individual accuracies.
Recall @ K indicates how many items the user likes in the test set appear in the recommendation list.
The recommendation recall rate for a single user u is:
Figure GDA0002888269470000153
wherein:
hit represents how many of the user u's recommendation list are actually purchased by the user u.
Figure GDA0002888269470000154
The number of products actually purchased for user u.
The overall recall ratio was:
Figure GDA0002888269470000155
i.e. the recall of the population sample is the average of the individual recall.
MRR @ K is specifically:
Figure GDA0002888269470000161
wherein:
rankuindicating the location of the first predicted accurate product in the recommendation list of user u.
MAP @ K is specifically:
Figure GDA0002888269470000162
Figure GDA0002888269470000163
wherein:
hit represents how many of the recommendation list of user u hit the product that user u actually purchased.
An embodiment of the present invention further provides a product recommendation system based on social contact and community, where the system includes a computer, and the computer includes:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
acquiring interaction data, social relationship data and community relationship data of a user and a product, and forming historical data;
determining a feedback relationship between the user and the product based on the historical data and a preset partial order relationship;
determining a preference relationship of the user to the product based on the historical data and the feedback relationship;
determining an objective function of the preference relationship;
acquiring a preference score of a user for a product based on the target function;
and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result.
It can be understood that the recommendation system provided by the embodiment of the present invention corresponds to the recommendation method, and the explanation, examples, and beneficial effects of the relevant contents may refer to the corresponding contents in the product recommendation method based on social contact and community, which are not described herein again.
An embodiment of the present invention further provides a computer-readable storage medium, where at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method.
In summary, compared with the prior art, the method has the following beneficial effects:
according to the method and the device, historical data are formed by acquiring the interaction data, social relation data and community relation data of the user and the product; determining a feedback relationship between a user and a product based on historical data and a preset partial order relationship; determining a preference relationship of the user to the product based on the historical data and the feedback relationship; determining an objective function of the preference relationship; acquiring a preference score of a user for a product based on a target function; and sequencing the non-interactive products of the user based on the preference scores to obtain a product recommendation result. According to the embodiment of the invention, the interaction between the user and the product is analyzed by combining the social relationship and the community relationship of the user, and the preference of the user is divided in a finer granularity, so that the recommendation result is more accurate.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A social and community based product recommendation method, the recommendation method being executed by a computer and comprising the steps of:
acquiring interaction data, social relationship data and community relationship data of a user and a product, and forming historical data;
determining a feedback relationship between the user and the product based on the historical data and a preset partial order relationship;
determining a preference relationship of the user to the product based on the historical data and the feedback relationship;
determining an objective function of the preference relationship;
acquiring a preference score of a user for a product based on the target function;
ranking the non-interactive products of the user based on the preference scores to obtain a product recommendation result;
the preference relation of the user to the product is represented by a matrix decomposition model based on the user set U to the product set P:
Figure FDA0002888269460000011
wherein:
Figure FDA0002888269460000012
representing a preference set of the user set U to the product set P;
w represents a feature matrix of the user set U;
h represents a feature matrix of the product set P;
b represents a deviation item of the product set P;
the objective function of the preference relationship is:
Figure FDA0002888269460000021
wherein:
Figure FDA0002888269460000022
indicating that user u is facing feedback set PFuProduct p of ChineseiA preference for (c);
Figure FDA0002888269460000023
representing a set SF of social feedback for a user uuProduct p of ChinesesA preference for (c);
Figure FDA0002888269460000024
representing a set GF of user u-to-community feedbackuProduct p of ChinesegA preference for (c);
Figure FDA0002888269460000025
representing user u versus negative feedback set NFuProduct p of ChinesejA preference for (c);
σ (-) represents a logistic function;
Θ denotes a set of parameters in the matrix decomposition model, i.e., Θ ═ { W, H, b };
λΘis a regularization parameter;
CSrepresenting user u and product psWithout interaction, but of user uThe number of friends who have interacted with the product in the friends;
Cgrepresenting user u and product pgAnd no interaction exists, and the social friends of the user u have no interaction with the product, but the social friends of the user u have the number of the interactive social friends with the product.
2. The recommendation method of claim 1, wherein the historical data comprises:
the system comprises a user set U, a product set P, a set D in which users and products have interaction, a social relationship set S and a community relationship set G.
3. The recommendation method according to claim 2, wherein the preset partial order relationship is:
positive feedback > social feedback > community feedback > negative feedback;
the feedback relationship between the user and the product comprises:
and (3) positive feedback assembly: PF (particle Filter)u＝{＜u,pi＞}，
Wherein: < u, piIs e.g. D, represents user u and product piInteraction exists;
social feedback set: SFu＝{＜u,ps＞}，
Wherein: < u, psThe social friends of the user u have interaction, but the user u does not have interactive products;
community feedback set: GFu＝{＜u,pg＞}，
Wherein: < u, pgProducts that show that social friends of the user u have interaction, but the user u does not interact with the social friends of the user u and does not interact with the social friends of the user u;
and (3) negative feedback integration: NFu＝{＜u,pj＞}，
Wherein: < u, pjThe expression > represents that the user u does not interact with the social friends and the social group friends of the user u, and the social group friends of the user u also do not have interactive products.
4. The recommendation method according to claim 1, wherein the obtaining of the preference score of the user for the product based on the objective function specifically comprises:
s501, setting the iteration number as iter, wherein iter is 1;
s502, initializing a parameter set at random based on normal distribution:
Θiter＝{Witer,Hiter,biter}
s503, setting a circulation variable to be eta, wherein eta is 1;
s504, traversing an interaction relation set D of the user and the product under the iter iteration;
s505, in the eta cycle process, one user u is randomly selected, and meanwhile, a corresponding positive feedback product p is taken out from a positive feedback set, a social feedback set, a community feedback set and a negative feedback set corresponding to the user uiSocial feedback product psCommunity feedback product pgAnd negative feedback product pj(ii) a Thereby obtaining a group of user partial order product combinations of the eta traversal under the iter iteration
Figure FDA0002888269460000041
S506, combining the products in the user partial order
Figure FDA0002888269460000042
Substituting the target function into the target function to obtain the target function of the eta cycle under the iter iteration
Figure FDA0002888269460000043
S507, updating the objective function based on the random gradient rise method
Figure FDA0002888269460000044
Middle parameter
Figure FDA0002888269460000045
And
Figure FDA0002888269460000046
a gradient of (a);
s508, assigning eta +1 to eta, judging whether eta > | D | is true, and if yes, executing the step S509; otherwise, returning to step S505;
s509, judging parameters
Figure FDA0002888269460000047
Whether convergence is achieved, if convergence is achieved, an optimal parameter set is obtained
Figure FDA0002888269460000048
Otherwise, iter +1 is assigned to iter, and the procedure returns to step S503.
5. The recommendation method of claim 2, wherein the obtaining of the product recommendation result specifically comprises:
and randomly selecting a user v in the product set U, and sequencing all non-interactive products of the user v in the product set P in a descending order according to the preference score to obtain a product recommendation result.
6. The recommendation method of claim 1, further comprising:
evaluating the product recommendation, the evaluating comprising: precision @ K assessment, Recall @ K assessment, MRR @ K assessment, MAP @ K assessment.
7. A social and community based product recommendation system, the recommendation system comprising a computer, the computer comprising:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
acquiring interaction data, social relationship data and community relationship data of a user and a product, and forming historical data;
determining a feedback relationship between the user and the product based on the historical data and a preset partial order relationship;
determining a preference relationship of the user to the product based on the historical data and the feedback relationship;
determining an objective function of the preference relationship;
acquiring a preference score of a user for a product based on the target function;
ranking the non-interactive products of the user based on the preference scores to obtain a product recommendation result;
the preference relation of the user to the product is represented by a matrix decomposition model based on the user set U to the product set P:
Figure FDA0002888269460000051
wherein:
Figure FDA0002888269460000052
representing a preference set of the user set U to the product set P;
w represents a feature matrix of the user set U;
h represents a feature matrix of the product set P;
b represents a deviation item of the product set P;
the objective function of the preference relationship is:
Figure FDA0002888269460000061
wherein:
Figure FDA0002888269460000062
indicating that user u is facing feedback set PFuProduct p of ChineseiA preference for (c);
Figure FDA0002888269460000063
representing a set SF of social feedback for a user uuProduct p of ChinesesA preference for (c);
Figure FDA0002888269460000064
representing a set GF of user u-to-community feedbackuProduct p of ChinesegA preference for (c);
Figure FDA0002888269460000065
representing user u versus negative feedback set NFuProduct p of ChinesejA preference for (c);
σ (-) represents a logistic function;
Θ denotes a set of parameters in the matrix decomposition model, i.e., Θ ═ { W, H, b };
λΘis a regularization parameter;
CSrepresenting user u and product psThe number of friends who have interacted with the product in the friends of the user u is not interacted;
Cgrepresenting user u and product pgAnd no interaction exists, and the social friends of the user u have no interaction with the product, but the social friends of the user u have the number of the interactive social friends with the product.
8. A computer readable storage medium having stored thereon at least one instruction, at least the one instruction being loaded and executed by a processor to implement the method of claim 1.
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