Open AccessDesign of Hybrid Recommendation Algorithm Based on User Dynamic Behavior and Static Attributes
Author(s) -
Xiaoxiao Dai
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 - 10.1088/1742-6596/1881/2/022035
Subject(s) - collaborative filtering , computer science , recommender system , scalability , algorithm , dynamic data , data mining , point (geometry) , value (mathematics) , machine learning , database , mathematics , geometry
Collaborative filtering recommendation algorithm is one of the most successful recommendation algorithms, but the traditional collaborative filtering recommendation algorithm is proved to have a series of problems in data sparsity, cold boot and scalability. Based on the problems mentioned above, this paper analyses user dynamic behavior and a hybrid recommendation algorithm based on user dynamic behavior and static attributes (UDBSA) was proposed in this paper. By determining the optimal value of BP critical point for the number of ratings, the recommendation strategy was dynamically selected according to the number of ratings. This method can comprehensively alleviate the influence of problems of new users and concept drift related problem on recommendation results.