WO2020168992A1 - Product recommendation method, apparatus, and device and storage medium - Google Patents

Abstract

A product recommendation method, product recommendation apparatus and product recommendation device and a computer readable storage medium. The product recommendation method comprises: obtaining score data of different users for different products respectively, the score data being used for representing the preferences of the users for the products (S101); calculating the user similarity between the different users according to the score data of the different users for the products (S102); and obtaining score data of a target user for a product to be recommended according to score data of neighbor users of the target user for the product to be recommended, so as to recommend the product (S103). According to the product recommendation method based on neighbor propagation, the concept of iteration is employed, and score data of each round is brought into the next round of predictive scoring, so that a score matrix becomes more and more dense, and the predictive scoring becomes more and more accurate. Therefore, the product recommendation performance can be better improved.

Description

Commodity recommendation method, device, equipment and storage medium

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on February 18, 2019, the application number is 201910123041.2, and the title is "A method, device, equipment and storage medium for product recommendation", the entire contents of which are incorporated by reference In this application:

Technical field

The present invention relates to the field of information processing technology, in particular to a method, device, equipment and computer-readable storage medium for recommending commodities.

Background technique

The emergence and popularization of the Internet has brought a large amount of information to users, which has met their needs for information in the information age. However, with the rapid development of the Internet, the amount of information on the Internet has increased significantly, making users face a large amount of information. When you cannot get the information that is really useful to you, the efficiency of using the information is reduced. This is the so-called information overload problem.

A very potential way to solve the problem of information overload is the recommendation system, which is a personalized information recommendation system that recommends information and products that interest users to users based on their information needs and interests. Compared with search engines, the recommendation system conducts personalized calculations by studying the user's interest preferences, and the system discovers the user's points of interest, thereby guiding users to discover their own information needs.

Recommender systems are now widely used in many fields, among which the most typical field with good development and application prospects is e-commerce. For personalized recommendation systems, some traditional similarity measurement methods such as cosine similarity, Pearson Correlation Coefficient (PCC), and matrix factorization (Matrix factorization) algorithms have been widely used in product recommendation algorithms. However, as the number of users and commodities continues to increase, the sparsity of the rating matrix is becoming more and more obvious. Calculation based on similarity and matrix decomposition can no longer improve recommendation performance.

Summary of the invention

The purpose of the present invention is to provide a product recommendation method, device, device and computer readable storage medium to solve the problem of poor recommendation performance on sparse scoring data.

In order to solve the above technical problems, the present invention provides a method for recommending commodities, including:

Acquiring rating data of different users for different commodities, where the rating data is used to characterize the user's degree of preference for the commodity;

Calculating the user similarity between the different users according to the scoring data of the products by different users;

According to the score data of the product to be recommended by the neighbor users of the target user, the score data of the product to be recommended by the target user is obtained to recommend the product.

Optionally, the obtaining rating data of different users for different commodities includes:

Set U = {u ₁ ,...,u _l } and M = {m ₁ ,...,m _n } to represent the user collection and the product collection, l and n represent the number of users and the number of products, enter the product Scoring matrix

Where r _ij ∈ {0,s} represents the rating of the user u _i on the product m _j ;

If the value of r _ij is 0, it means that the user has not rated the product, and if the value of r _{ij is} non-zero, it means that the user has rated the product.

Optionally, the calculating the user similarity between the different users according to the rating data of the products by different users includes:

For any two users u _g and u _i , S(u _g ,u _i )=A(u _g ,u _i )C(u _g ,u _i )PCC(u _g ,u _i ) User similarity;

Among them, A(u _g ,u _i ) represents the degree of consistency between user u _g and user u _i , and the calculation method is:

V _g represents a rating vector based on the user u _g ,

The superscript T denotes the transpose, V _i represents a vector of the user based on the rating of u _{i; C (u g, u i} ) represents a trade-off factor, calculated as:

I _g is the set of items evaluated by the user u _g , and I _i is the set of items evaluated by the user u _i ; PCC(u _g ,u _i ) is to calculate the Pearson similarity between two users, the calculation method is:

Among them, r _gj is the rating of the item m _j by the user u _g ,

Indicates the average value of the non-zero ratings of the product by the user u _i ,

Represents the average value of the non-zero ratings of the user u _g on the product; r _ij is the rating of the user u _i on the item m _j ; I _g ∩I _i represents the set of products that the user u _g and the user u _{i have} jointly evaluated.

Optionally, the obtaining the scoring data of the target user for the product to be recommended according to the score data of the neighboring users of the target user for the product to be recommended includes:

In the target user u _t ∈U, the target user to be recommended product recommendation u _{t m a ∈ {m j | m} j ∈M∧r tj = 0} , the prediction target to be recommended to the user u _t commodities m _a rating

for:

_{Wherein, m a ∈ {m j |} m j ∈M∧r tj = 0} goods on behalf of the user has not ratings, ∧ - a connection condition symbol, r _ca user u _c represents the score of product m _a,

Indicates the average value of the target user u _t ’s non-zero score for the product,

Represents the average value of non-zero scores of neighboring users u _c on the product, S(u _t , u _c ) represents the user similarity between target user u _t and neighboring user u _c , NK is the number set of neighboring users of target user u _t , including the target user's user selected from high to low similarity K nearest neighbors users, W _ca m _a treatment recommended product for neighbor users u _c scores trust and W _ca = decay ^itear, decay is the decay rate, itear number of iterations , Each round of newly added points corresponds to a trust coefficient, and each round is multiplied by the decay rate.

This application also provides a product recommendation device, including:

The obtaining module is used to obtain rating data of different users for different products, and the rating data is used to characterize the user's preference for the products;

The calculation module is used to calculate the user similarity between the different users according to the rating data of the products by different users;

The recommendation module is used to obtain the score data of the target user for the product to be recommended according to the score data of the neighboring users of the target user to recommend the product, so as to recommend the product.

Optionally, the acquisition module is used to:

Set U = {u ₁ ,...,u _l } and M = {m ₁ ,...,m _n } to represent the user collection and the product collection, l and n represent the number of users and the number of products, enter the product Scoring matrix

Where ri _j ∈ {0,s} represents the user u _i 's rating of the product m _j ;

If the value of r _ij is 0, it means that the user has not rated the product, and if the value of r _{ij is} non-zero, it means that the user has rated the product.

Optionally, the calculation module is used for:

For any two users u _g and u _i , S(u _g ,u _i )=A(u _g ,u _i )C(u _g ,u _i )PCC(u _g ,u _i ) User similarity;

Among them, A(u _g ,u _i ) represents the degree of consistency between user u _g and user u _i , and the calculation method is:

V _g represents a rating vector based on the user u _g ,

The superscript T denotes the transpose, V _i represents a vector of the user based on the rating of u _{i; C (u g, u i} ) represents a trade-off factor, calculated as:

I _g is the set of items evaluated by the user u _g , and I _i is the set of items evaluated by the user u _i ; PCC(u _g ,u _i ) is to calculate the Pearson similarity between two users, the calculation method is:

Among them, r _gj is the rating of the item m _j by the user u _g ,

Indicates the average value of the non-zero ratings of the product by the user u _i ,

Represents the average value of the non-zero ratings of the user u _g on the product; r _ij is the rating of the user u _i on the item m _j ; I _g ∩I _i represents the set of products that the user u _g and the user u _{i have} jointly evaluated.

Optionally, the recommendation module is used for:

In the target user u _t ∈U, the target user to be recommended product recommendation u _{t m a ∈ {m j | m} j ∈M∧r tj = 0} , the prediction target to be recommended to the user u _t commodities m _a rating

for:

_{Wherein, m a ∈ {m j |} m j ∈M∧r tj = 0} goods on behalf of the user has not ratings, ∧ - a connection condition symbol, r _ca user u _c represents the score of product m _a,

Indicates the average value of the target user u _t ’s non-zero score for the product,

Represents the average value of non-zero scores of neighboring users u _c on the product, S(u _t , u _c ) represents the user similarity between target user u _t and neighboring user u _c , NK is the number set of neighboring users of target user u _t , including the target user's user selected from high to low similarity K nearest neighbors users, W _ca m _a treatment recommended product for neighbor users u _c scores trust and W _ca = decay ^itear, decay is the decay rate, itear number of iterations , Each round of newly added points corresponds to a trust coefficient, and each round is multiplied by the decay rate.

This application also provides a product recommendation device, including:

Memory, used to store computer programs;

The processor is used to implement the steps of any one of the aforementioned commodity recommendation methods when the computer program is executed.

The present application also provides a computer-readable storage medium having a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of any one of the aforementioned commodity recommendation methods are implemented.

The commodity recommendation method provided by the present invention obtains the rating data of different users for different commodities, and the rating data is used to characterize the user's degree of preference for the commodity; according to the rating data of different users on the commodity, the user similarity between different users The degree is calculated; according to the scoring data of the products to be recommended by the neighbor users of the target user, the scoring data of the products to be recommended by the target user is obtained to recommend the products. This application is based on the product recommendation method of neighbor propagation and adopts an iterative idea to bring each round of scoring data into the next round of prediction scoring, making the scoring matrix denser and more accurate. Therefore, this application can better improve the performance of product recommendation. In addition, this application also provides a product recommendation device, equipment, and computer-readable storage medium with the above technical effects.

Description of the drawings

In order to explain the embodiments of the present invention or the technical solutions of the prior art more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are merely For some of the embodiments of the present invention, those of ordinary skill in the art can obtain other drawings based on these drawings without creative work.

FIG. 1 is a flowchart of a specific implementation manner of the commodity recommendation method provided by this application;

2 is a structural block diagram of a product recommendation device provided by an embodiment of the present invention;

Figure 3 is a block diagram of the product recommendation device provided by this application.

detailed description

As the number of users and products continues to increase, the sparsity of the rating matrix becomes more and more obvious. Calculation based on similarity and matrix decomposition can no longer improve recommendation performance. The core of the present invention is to provide a commodity recommendation method, device, equipment and computer-readable storage medium to solve the above technical problems.

In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

A flowchart of a specific implementation manner of the commodity recommendation method provided by the present application is shown in FIG. 1, and the method includes:

Step S101: Obtain scoring data of different users for different commodities, and the scoring data is used to characterize the user's degree of preference for the commodity;

Set U = {u ₁ ,...,u _l } and M = {m ₁ ,...,m _n } to represent the user collection and the product collection, l and n represent the number of users and the number of products, enter the product Scoring matrix

Among them, r _ij ∈ {0, s} represents the user u _i ’s score on the product m _j ; if the value of r _ij is 0, it means that the user has not evaluated the product, and if r _{ij is} non-zero, it represents the user’s score on the product evaluation. value. The size of the score indicates how high or low users like the product.

Step S102: Calculate the user similarity between the different users according to the rating data of the products by different users;

Specifically, ACCCC (Accordance and Compromise based Pearson Correlation Coefficient) similarity is used to calculate the user similarity between different users. For any two users u _g and u _i , S(u _g ,u _i )=A(u _g ,u _i )C(u _g ,u _i )PCC(u _g ,u _i ) User similarity;

Among them, A(u _g ,u _i ) represents the degree of consistency between user u _g and user u _i , and the calculation method is:

V _g represents a rating vector based on the user u _g ,

The superscript T denotes the transpose, V _i represents a vector of the user based on the rating of u _{i; C (u g, u i} ) represents a trade-off factor, calculated as:

I _g is the set of items evaluated by the user u _g , and I _i is the set of items evaluated by the user u _i ; PCC(u _g ,u _i ) is to calculate the Pearson similarity between two users, the calculation method is:

Among them, r _gj is the rating of the item m _j by the user u _g ,

Indicates the average value of the non-zero ratings of the product by the user u _i ,

Represents the average value of the non-zero ratings of the user u _g on the product; r _ij is the rating of the user u _i on the item m _j ; I _g ∩I _i represents the set of products that the user u _g and the user u _{i have} jointly evaluated. For example, the user u _g score is {1,3,4,2,1,1,2}, then

It is equal to 2.

Step S103: Obtain the score data of the target user for the product to be recommended according to the score data of the neighboring users of the target user to recommend the product.

In the target user u _t ∈U, the target user to be recommended product recommendation u _{t m a ∈ {m j | m} j ∈M∧r tj = 0} , the prediction target to be recommended to the user u _t commodities m _a rating

for:

_{Wherein, m a ∈ {m j |} m j ∈M∧r tj = 0} goods on behalf of the user has not ratings, ∧ - a connection condition symbol, and meaning expressed; u _c ∈NK∧r _ca> 0 indicates two conditions At the same time, NK is the number set of neighbor users of the target user u _t , including K neighbor users selected from high to low similarity to the target user, and then select users who have rated product a from these users. r _ca user u _c represents the score of product m _a,

Indicates the average value of the target user u _t ’s non-zero score for the product,

Represents the average value of non-zero scores of neighboring users u _c on the product, S(u _t , u _c ) represents the user similarity between target user u _t and neighboring user u _c , NK is the number set of neighboring users of target user u _t , including the target user's user selected from high to low similarity K nearest neighbors users, W _ca m _a treatment recommended product for neighbor users u _c scores trust and W _ca = decay ^itear, decay is the decay rate, itear number of iterations , Each round of newly added points corresponds to a trust coefficient, and each round is multiplied by the decay rate.

The commodity recommendation method provided by the present invention obtains the rating data of different users for different commodities, and the rating data is used to characterize the user's degree of preference for the commodity; according to the rating data of different users on the commodity, the user similarity between different users The degree is calculated; according to the scoring data of the products to be recommended by the neighbor users of the target user, the scoring data of the products to be recommended by the target user is obtained to recommend the products. This application is based on the product recommendation method of neighbor propagation and adopts an iterative idea to bring each round of scoring data into the next round of prediction scoring, making the scoring matrix denser and more accurate. Therefore, this application can better improve the performance of product recommendation.

The product recommendation method provided by this application will be further elaborated with a specific embodiment below. This embodiment of the application selects the Movielens data set ML_100k data for testing. The data set consists of 942 users, 1682 movies, and a total of score records up to As many as 100,000. The score range of this data set is {1,2,3,4,5}, 1 means a hate movie, 5 means a favorite movie. The sparseness of the data set reaches 93.7% to form a 942*1682 rating matrix, in which the unrated movies are represented by 0. Set out 80% as the training set, and the rest as the test set.

The specific implementation steps are as follows:

Let set U={u ₁ ,...,u _l } and M={m ₁ ,...,m _n } represent the user collection and the product collection, respectively, and l and n represent the number of users and the number of products, respectively. In this embodiment, l=942 and n=1682.

Enter product rating matrix

Among them, r _ij ∈ {0, s} represents the rating of the product m _j by the user u _i . If the value of r _ij is 0, it means that the user has not evaluated the product, and if r _ij takes other values, it means that the user has evaluated the product, and the value is the score. The size of the scoring value indicates how high or low the user likes the product. In this embodiment, s is 5.

ACCCC (Accordance and Compromise based Pearson Correlation Coefficient) similarity is used to calculate the user similarity between different users. For any two users u _g and u _i , the similarity between them is calculated as follows:

S(u _g ,u _i )=A(u _g ,u _i )C(u _g ,u _i )PCC(u _g ,u _i )

Where A(u _g ,u _i ) represents the degree of consistency between u _g and neighboring users u _i , and the calculation method is:

V _g represents a rating vector based on user u _g ; C(u _g ,u _i ) represents a compromise factor, considering the percentage of common ratings between two users, the calculation method is:

Where I _g is a collection of items evaluated by the user u _g . PCC(u _g ,u _i ) is to calculate the Pearson similarity between two users, the calculation method is:

Where r _gj is the rating of user u _g on item m _j ,

Represents the average value of the non-zero ratings of the product by the user u _i .

Assuming that the target user u _t ∈U, the target user u _t recommend an item _{m a ∈ {m j | m} j ∈M∧r tj = 0}, i.e. the prediction target user u _t m _a product score of

among them

Indicates the average value of the non-zero ratings of the products by users u _t ,

Represents the average value of non-zero scores of neighboring users u _c for the product, S(u _t , u _c ) represents the similarity between target user u _t and neighboring user u _c , NK is the number set of neighboring users of target user u _t , including users Low degree of similarity selected users K, W _ca score for the user u _c m _a product of confidence and

W _ca ＝decay ^itear

Where decay is the decay rate, and itear is the number of iterations. Each round of newly added points must have a trust factor, which is multiplied by the decay rate for each round. In this embodiment, the value of K is 40, decay is 0.9, and itear is 15 times.

The effect of the present invention can be verified by the following experiment: the data set is randomly divided into five training sets and test sets. In the recommendation prediction, the comparison method includes the present invention, user-based PCC similarity (abbreviated as UCF-PCC) and commodity-based Pearson similarity algorithm (abbreviated as ICF-PCC) and non-iterative prediction similarity (ACPCC) .

Use the absolute value error index (MAE), square error index (RMSE) and recall rate (Recall) to evaluate the recommendation effect:

Where l'and n'are the number of users and the number of movies on the test set, IR _gt is the set of movies liked by the user on the test set, and IR _gp is the set of movies recommended to the user. The results are shown in Table 1. It can be seen that the recommended performance of the present invention is significantly better than other comparison methods.

Table 1 Comparison of recommended results of the three algorithms

recommend	MAE	RMSE	Recall
UCF-PCC	0.9838	1.1174	0.2607
ICF-PCC	0.8716	1.0196	0.0571
ACPCC	0.7689	0.9771	0.7056
this invention	0.7445	0.9569	0.7295

The product recommendation device provided by the embodiment of the present invention will be introduced below. The product recommendation device described below and the product recommendation method described above can be referred to each other.

FIG. 2 is a structural block diagram of a product recommendation device provided by an embodiment of the present invention. Referring to FIG. 2, the product recommendation device may include:

The obtaining module 100 is configured to obtain rating data of different users for different commodities, and the rating data is used to characterize the user's degree of preference for the commodity;

The calculation module 200 is configured to calculate the user similarity between the different users according to the rating data of the products by different users;

The recommendation module 300 is configured to obtain the score data of the target user for the product to be recommended according to the score data of the neighboring users of the target user for the product to be recommended, so as to recommend the product.

As a specific implementation manner, the acquisition module in the recommendation device provided in this application is used to:

Set U = {u ₁ ,...,u _l } and M = {m ₁ ,...,m _n } to represent the user collection and the product collection, l and n represent the number of users and the number of products, enter the product Scoring matrix

Where r _ij ∈ {0,s} represents the rating of the user u _i on the product m _j ;

If the value of r _ij is 0, it means that the user has not rated the product, and if the value of r _{ij is} non-zero, it means that the user has rated the product.

As a specific implementation, the calculation module in the recommendation device provided in this application is used to:

For any two users u _g and u _i , S(u _g ,u _i )=A(u _g ,u _i )C(u _g ,u _i )PCC(u _g ,u _i ) User similarity;

Among them, A(u _g ,u _i ) represents the degree of consistency between user u _g and user u _i , and the calculation method is:

V _g represents a rating vector based on user u _g ; C(u _g ,u _i ) represents a compromise factor, and the calculation method is:

I _g is the set of items evaluated by the user u _g , PCC(u _g ,u _i ) is to calculate the Pearson similarity between two users, the calculation method is:

Among them, r _gj is the rating of the item m _j by the user u _g ,

Represents the average value of the non-zero ratings of the product by the user u _i .

As a specific implementation manner, the recommendation module in the recommendation device provided in this application is used to:

In the target user u _t ∈U, the target user to be recommended product recommendation u _{t m a ∈ {m j | m} j ∈M∧r tj = 0} , the prediction target to be recommended to the user u _t commodities m _a rating

for:

among them,

Indicates the average value of the target user u _t ’s non-zero score for the product,

Represents the average value of non-zero scores of neighboring users u _c on the product, S(u _t , u _c ) represents the user similarity between target user u _t and neighboring user u _c , NK is the number set of neighboring users of target user u _t , including the target user's user selected from high to low similarity K nearest neighbors users, W _ca m _a treatment recommended product for neighbor users u _c scores trust and W _ca = decay ^itear, decay is the decay rate, itear number of iterations , Each round of newly added points corresponds to a trust coefficient, and each round is multiplied by the decay rate.

The product recommendation device of this embodiment is used to implement the aforementioned product recommendation method. Therefore, the specific implementation of the product recommendation device can be seen in the previous embodiment of the product recommendation method, for example, the acquisition module 100, the calculation module 200, and the recommendation module 300. , Are respectively used to implement steps S101, S102, and S103 in the foregoing commodity recommendation method. Therefore, for the specific implementation, refer to the description of the respective parts of the embodiment, and details are not described herein again.

The commodity recommendation method provided by the present invention obtains the rating data of different users for different commodities, and the rating data is used to characterize the user's degree of preference for the commodity; according to the rating data of different users on the commodity, the user similarity between different users The degree is calculated; according to the scoring data of the products to be recommended by the neighbor users of the target user, the scoring data of the products to be recommended by the target user is obtained to recommend the products. This application is based on the product recommendation method of neighbor propagation, and adopts the idea of iteration to bring the scoring data of each round to the next round of prediction scoring, making the scoring matrix more and more dense, and also making the prediction score more and more accurate. Therefore, this application can better improve the performance of product recommendation.

In addition, this application also provides a product recommendation device. As shown in Fig. 3, the product recommendation device structure block diagram provided in this application, the device includes:

The memory 11 is used to store computer programs;

The processor 12 is configured to implement the steps of any one of the aforementioned commodity recommendation methods when executing the computer program.

In addition, the present application also provides a computer-readable storage medium having a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of any one of the aforementioned commodity recommendation methods are implemented.

To sum up, this application obtains the rating data of different users for different products, and the rating data is used to characterize the user's preference for the product; according to the rating data of different users to the product, the user similarity between different users is calculated; According to the scoring data of the products to be recommended by the neighboring users of the target user, the scoring data of the products to be recommended by the target user is obtained to recommend the products. This application is based on the product recommendation method of neighbor propagation and adopts an iterative idea to bring each round of scoring data into the next round of prediction scoring, making the scoring matrix denser and more accurate. Therefore, this application can better improve the performance of product recommendation.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method part.

Professionals may further realize that the units and algorithm steps of the examples described in the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of both, in order to clearly illustrate the possibilities of hardware and software. Interchangeability. In the above description, the composition and steps of each example have been generally described in accordance with the function. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present invention.

The steps of the method or algorithm described in the embodiments disclosed in this document can be directly implemented by hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or all areas in the technical field. Any other known storage medium.

The commodity recommendation method, device, equipment, and computer-readable storage medium provided by the present invention have been introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation of the present invention. The description of the above examples is only used to help understand the method and core idea of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims (10)

1. A product recommendation method, characterized in that it includes:

   Acquiring rating data of different users for different commodities, where the rating data is used to characterize the user's degree of preference for the commodity;

   Calculating the user similarity between the different users according to the scoring data of the products by different users;

   According to the score data of the product to be recommended by the neighbor users of the target user, the score data of the product to be recommended by the target user is obtained to recommend the product.
2. 5. The product recommendation method according to claim 1, wherein said obtaining score data of different users for different products comprises:

   Set U = {u ₁ ,...,u _l } and M = {m ₁ ,...,m _n } to represent the user collection and the product collection, l and n represent the number of users and the number of products, enter the product Scoring matrix

   

   Where r _ij ∈ {0,s} represents the rating of the user u _i on the product m _j ;

   If the value of r _ij is 0, it means that the user has not rated the product, and if the value of r _{ij is} non-zero, it means that the user has rated the product.
3. The method for recommending a product according to claim 2, wherein the calculating the user similarity between the different users according to the rating data of the products by different users comprises:

   For any two users u _g and u _i , S(u _g ,u _i )=A(u _g ,u _i )C(u _g ,u _i )PCC(u _g ,u _i ) User similarity;

   Among them, A(u _g ,u _i ) represents the degree of consistency between user u _g and user u _i , and the calculation method is:

   

   V _g represents a rating vector based on the user u _g ,

   

   The superscript T denotes the transpose, V _i represents a vector of the user based on the rating of u _{i; C (u g, u i} ) represents a trade-off factor, calculated as:

   

   I _g is the set of items evaluated by the user u _g , and I _i is the set of items evaluated by the user u _i ; PCC(u _g ,u _i ) is to calculate the Pearson similarity between two users, the calculation method is:

   

   Among them, r _gj is the rating of the item m _j by the user u _g ,

   

   Indicates the average value of the non-zero ratings of the product by the user u _i ,

   

   Represents the average value of the non-zero ratings of the user u _g on the product; r _ij is the rating of the user u _i on the item m _j ; I _g ∩I _i represents the set of products that the user u _g and the user u _{i have} jointly evaluated.
4. 5. The product recommendation method according to claim 3, wherein the obtaining the target user's score data for the product to be recommended according to the score data of the neighbor users of the target user for the product to be recommended comprises:

   In the target user u _t ∈U, the target user to be recommended product recommendation u _{t m a ∈ {m j | m} j ∈M∧r tj = 0} , the prediction target to be recommended to the user u _t commodities m _a rating

   

   for:

   

   _{Wherein, m a ∈ {m j |} m j ∈M∧r tj = 0} goods on behalf of the user has not ratings, ∧ - a connection condition symbol, r _ca user u _c represents the score of product m _a,

   

   Indicates the average value of the target user u _t ’s non-zero score for the product,

   

   Represents the average value of non-zero scores of neighboring users u _c on the product, S(u _t , u _c ) represents the user similarity between target user u _t and neighboring user u _c , NK is the number set of neighboring users of target user u _t , including the target user's user selected from high to low similarity K nearest neighbors users, W _ca m _a treatment recommended product for neighbor users u _c scores trust and W _ca = decay ^itear, decay is the decay rate, itear number of iterations , Each round of newly added points corresponds to a trust coefficient, and each round is multiplied by the decay rate.
5. A product recommendation device, characterized in that it comprises:

   The obtaining module is used to obtain rating data of different users for different products, and the rating data is used to characterize the user's preference for the products;

   The calculation module is used to calculate the user similarity between the different users according to the rating data of the products by different users;

   The recommendation module is used to obtain the score data of the target user for the product to be recommended according to the score data of the neighboring users of the target user to recommend the product, so as to recommend the product.
6. The product recommendation device according to claim 5, wherein the acquisition module is used for:

   Set U = {u ₁ ,...,u _l } and M = {m ₁ ,...,m _n } to represent the user collection and the product collection, l and n represent the number of users and the number of products, enter the product Scoring matrix

   

   Where r _ij ∈ {0,s} represents the rating of the user u _i on the product m _j ;

   If the value of r _ij is 0, it means that the user has not rated the product, and if the value of r _{ij is} non-zero, it means that the user has rated the product.
7. The product recommendation device according to claim 6, wherein the calculation module is used for:

   For any two users u _g and u _i , S(u _g ,u _i )=A(u _g ,u _i )C(u _g ,u _i )PCC(u _g ,u _i ) User similarity;

   Among them, A(u _g ,u _i ) represents the degree of consistency between user u _g and user u _i , and the calculation method is:

   

   V _g represents a rating vector based on the user u _g ,

   

   The superscript T denotes the transpose, V _i represents a vector of the user based on the rating of u _{i; C (u g, u i} ) represents a trade-off factor, calculated as:

   

   I _g is the set of items evaluated by the user u _g , and I _i is the set of items evaluated by the user u _i ; PCC(u _g ,u _i ) is to calculate the Pearson similarity between two users, the calculation method is:

   

   Among them, r _gj is the rating of the item m _j by the user u _g ,

   

   Indicates the average value of the non-zero ratings of the product by the user u _i ,

   

   Represents the average value of the non-zero ratings of the user u _g on the product; r _ij is the rating of the user u _i on the item m _j ; I _g ∩I _i represents the set of products that the user u _g and the user u _{i have} jointly evaluated.
8. 8. The product recommendation device of claim 7, wherein the recommendation module is used for:

   In the target user u _t ∈U, the target user to be recommended product recommendation u _{t m a ∈ {m j | m} j ∈M∧r tj = 0} , the prediction target to be recommended to the user u _t commodities m _a rating

   

   for:

   

   _{Wherein, m a ∈ {m j |} m j ∈M∧r tj = 0} goods on behalf of the user has not ratings, ∧ - a connection condition symbol, r _ca user u _c represents the score of product m _a,

   

   Indicates the average value of the target user u _t ’s non-zero score for the product,

   

   Represents the average value of non-zero scores of neighboring users u _c on the product, S(u _t , u _c ) represents the user similarity between target user u _t and neighboring user u _c , NK is the number set of neighboring users of target user u _t , including the target user's user selected from high to low similarity K nearest neighbors users, W _ca m _a treatment recommended product for neighbor users u _c scores trust and W _ca = decay ^itear, decay is the decay rate, itear number of iterations , Each round of newly added points corresponds to a trust coefficient, and each round is multiplied by the decay rate.
9. A product recommendation device, characterized in that it includes:

   Memory, used to store computer programs;

   The processor is configured to implement the steps of the commodity recommendation method according to any one of claims 1 to 4 when the computer program is executed.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the method for recommending a product according to any one of claims 1 to 4 step.

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