A Collaborative Filtering Recommendation Method Based on Mixed Interest Similarity

Abstract

The invention provides a novel hybrid user interest similarity calculation method. A scoring matrix of used items is established by a user, when it is found that the scoring matrix of the user is empty, the similarity of characteristic attributes of the users is calculated, and similar users are searched for forecasting scores. When the number of articles commonly scored between a target user andother users is relatively small, the similarity degree of the articles is calculated, and the interest similarity of the users is indirectly calculated, wherein user interest similarity calculation ismainly divided into the three parts that the distance value of user scores is directly calculated, the contribution values of a set of scores are worked out and whether the set of scores are singularvalues or not in a whole scoring system is judged; finally, through the three user interest similarity calculation methods, smooth transition from similarity calculation according to the user attributes to similarity calculation according to user scoring information under the cold start state is achieved through a sigmoid function. The prediction scores of non-scored items of the target user arecalculated according to the user interest similarity, and N items with the highest predicted scores are selected to be recommended. By means of the method, the problems of cold start and data sparsitycan be effectively relieved, and the accuracy of forecast recommendation can be effectively improved.

Description

[0001] (1) Technical field [0002] The invention relates to the technical field of personalized recommendation, in particular to a collaborative filtering recommendation method based on mixed interest similarity. [0003] (2) Background technology [0004] A recommender system is an intelligent system that addresses information overload. Collaborative filtering is one of the methods of today's recommendation technology. It directly predicts the items that users may like based on their historical behavior. It is widely used in almost all large-scale e-commerce websites. However, when a user first enters the system, there is no rating record, and the number of items jointly rated by users is too small, which leads to data sparsity. The user similarity calculated by the traditional similarity is not accurate enough. At this time, the mixture based on user interests Similarity can solve data sparsity and extract user interests. [0005] The key technology of user interest simila...

AI Technical Summary

Problems solved by technology

But ignore the transition of items from sparse to dense

Based on this, there may be some deviation in the calculation of user interest similarity

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