Cluster-based collaborative filtering commodity recommendation method and system

A product recommendation and collaborative filtering technology, applied in the field of collaborative filtering product recommendation methods and systems, can solve the problems of inaccurate recommendation results and difficulty in extracting content features.

Inactive Publication Date: 2013-11-27
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0004] The general collaborative filtering method has inaccurate recommendation results due to the sparsity problem, while the

Method used

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  • Cluster-based collaborative filtering commodity recommendation method and system
  • Cluster-based collaborative filtering commodity recommendation method and system
  • Cluster-based collaborative filtering commodity recommendation method and system

Examples

Experimental program
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Embodiment

[0063] Example: such as figure 1 As shown, we use the rating data of movielens. Movielens is a movie website. Users can rate the movies on the website after watching them. The score is 1-5. The higher the score, the more the user likes the movie. We obtained the data of 943 users in total. These users rated 100,000 times on 1,682 movie products of 18 types. In order to verify the accuracy of our recommendation results, we take out 20% of the scoring data as the test set. Our experimental result measurement method is measured by MAE mean absolute error, and the formula is as follows Here, N represents the total number of predicted scores, P i Denotes the i-th prediction score, R i Denotes the actual score of the i-th movie in the corresponding test set. The smaller the MAE, the more accurate our forecast is. In our experiments, we first investigate the effect of matrix sparsity on system recommendation performance. The sparsity is decreased by 10% and tested separately....

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Abstract

The invention belongs to the technical field of data mining and particularly relates to a cluster-based collaborative filtering commodity recommendation method and system. The method comprises obtaining scoring information of users on commodities and type tag information of the commodities through APIs (application program interfaces) of shopping websites; clustering the users through the types of commodities purchased by the users; according to the clustering results, assigning scoring assessment values for default scores in a user-commodity scoring matrix through a scoring assessment formula; calculating the similarity among the commodities in the matrix, predictively scoring the commodities not purchased by the users and recommending the top N commodities with the highest predictive scores to target users. Compared with the prior art, the cluster-based collaborative filtering commodity recommendation method and system has the advantages of solving the problem of data sparsity, reducing the problem of inconsistent scoring scales of various users, enabling the scoring similarity of users of the same type to be the highest and improving the accuracy of default assignment.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a product recommendation method and system based on clustering collaborative filtering. Background technique [0002] With the rapid development of e-commerce websites, recommender systems have been widely studied and applied. The recommendation system obtains user preferences by extracting and analyzing user information, behaviors, ratings, etc., to help e-commerce companies find specific users to recommend products they may purchase, and increase product sales. [0003] At present, some recommendation systems that have been widely studied use content-based recommendation algorithms, collaborative filtering recommendation algorithms, and so on. The content-based recommendation algorithm recommends similar products to the user through the characteristics of the products purchased by the user. The advantage of this algorithm is that it can handle the cold start p...

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Application Information

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IPC IPC(8): G06F17/30G06Q30/02
Inventor 刘云王琪曹伟王星桂畅旎
Owner BEIJING JIAOTONG UNIV
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