Open AccessResearch and Application of Algorithm Based on Maximum Expectation and Collaborative Filtering In Recommended System
Author(s) -
Ying Fan,
Huan Ma,
Chen Zhongyang,
Keli Shen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012205
Subject(s) - collaborative filtering , relevance (law) , computer science , recommender system , algorithm , preference , demographics , quality (philosophy) , function (biology) , matrix (chemical analysis) , data mining , information retrieval , mathematics , epistemology , evolutionary biology , sociology , biology , political science , law , composite material , philosophy , statistics , materials science , demography
The problem of sparse information is easily found in information searching for new users in recommended system, and it also has difficulty in recommending related modules. In view of the problem, the maximum expectation algorithm in demographics is adopted to cluster users for neighbouring users, and then it is regarded as an input of collaborative filtering algorithm. As the users’ scores on different projects represent their preference, certain demand relevance exists in similar users when evaluating the same project. And this kind of relevance degree varies with the change of individual demand. Therefore, a cooperative filtering algorithm based on the change of user’ demand is put forward by introducing a time weight function, which alleviates the shortness of traditional cooperative filtering recommendation algorithm. By tracking the needs of users, the scoring matrix can be predicted. According to experiments comparison, this algorithm can help solve the problem of sparseness of user’s scoring matrix and the recommendation quality is improved.