Personalized object recommending method based on object similarity and network structure
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A technology of network structure and recommendation method, applied in the Internet field, can solve the problems of loss of effective information, affecting the accuracy of recommendation results, waste, etc., and achieve the effect of improving accuracy
Active Publication Date: 2015-02-11
XIDIAN UNIV
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However, the recommendation method based on the resource allocation process on the two-part network only considers the user's browsing or purchase history, but does not consider the c
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Embodiment 1
[0028] Example 1, personalized item recommendation based on item category information similarity and network structure
[0029] Step 1: Construct an n×m proximity matrix A;
[0030] 1a) Define that the recommendation system has n items and m users. The items refer to recommended items such as movies, books and music. Taking movies as an example, if there are 1682 movies and 943 users in a movie recommendation system, then Take 1682 for n and 943 for m;
[0031] 1b) Define a two-part network graph structure as G(X,Y,E), where item node X is represented as x 1 ,x 2 ,...,x j ,...x n , the user node Y is denoted as y 1 ,y 2 ,...,y l ,...y m , E represents the edge of the two-part network graph structure, if user y l Items browsed or purchased x j , then connect the two nodes, where j is an integer from 1 to n, and l is an integer from 1 to n;
[0032] 1c) According to the above two-part network structure, an n×m proximity matrix is obtained
[0033] The value of ro...
Embodiment 2
[0049] Example 2, personalized item recommendation based on item rating information similarity and network structure
[0050] Step 1: Construct an n×m proximity matrix A;
[0051] (1a) Define that the recommendation system has n items and m users. Items refer to recommended items such as movies, books and music. Taking movies as an example, if there are 1682 movies and 943 users in a movie recommendation system, then n is 1682 , m takes 943;
[0052] (1b) Define the two-part network graph structure as G(X,Y,E), where the item node X is represented as x 1 ,x 2 ,...,x j ,...x n , the user node Y is denoted as y 1 ,y 2 ,...,y l ,...y m , E represents the edge of the two-part network graph structure, if user y l Items browsed or purchased x j , then connect the two nodes, where j is an integer from 1 to n, and l is an integer from 1 to n;
[0053] (1c) According to the above two-part network structure, an n×m proximity matrix is obtained
[0054] The value of row j...
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Abstract
The invention discloses a personalized object recommending method based on object similarity and a network structure. The personalized object recommending method comprises the following steps of 1, defining that a system has n projects and m users, and according to the project purchasing or browsing history of the users, building an n*m adjacency matrix; 2, according to the project information, building an n*m project approximate matrix; 3, calculating an n*n project resource distribution matrix by the adjacency matrix and the project resource distribution process; 4, according to the project resource distribution matrix and the project approximate matrix, calculating the comprehensive n*n project distribution matrix; 5, according to the initial resource distribution result of the first user and the comprehensive project distribution matrix, calculating the final project resource distribution, and according to the final project resource distribution results, descending the projects; 6, recommending the first S unbrowsed/unpurchased projects of the user to the user. The personalized object recommending method has the advantages that the accuracy of recommending results is improved, and the method can be used for recommending books, movies, music and the like.
Description
technical field [0001] The invention belongs to the technical field of the Internet, in particular to a method for recommending items, which is suitable for recommending books, movies, music and the like. Background technique [0002] With the rapid development of Internet technology, the amount of information in the network has increased sharply. However, on the one hand, this has brought about the problem of information overload, that is, the simultaneous presentation of too much information prevents users from obtaining the parts that are interesting and useful to them, which reduces the efficiency of information use; The information becomes dark information in the network and cannot be obtained by users. How to help users quickly find valuable information in massive data, and make the hidden information in the network accessible to users has become an urgent problem to be solved. Personalized recommendation system came into being, which is a very potential method to so...
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