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Media type:
E-Article
Title:
Collaborative Filtering Recommendation Algorithm Based on User Characteristics and User Interests
Contributor:
Li, Shangsong;
Li, Xuesong
Published:
IOP Publishing, 2020
Published in:
Journal of Physics: Conference Series, 1616 (2020) 1, Seite 012032
Language:
Not determined
DOI:
10.1088/1742-6596/1616/1/012032
ISSN:
1742-6588;
1742-6596
Origination:
Footnote:
Description:
Abstract Among many e-commerce platforms, collaborative filtering recommendation algorithm is currently the most widely used recommendation technology. In order to alleviate the deficiencies of the traditional user-based collaborative filtering algorithm in cold start and recommendation accuracy, this paper proposes a collaborative filtering recommendation algorithm based on user characteristics and user interests. The similarity of the algorithm in this paper is composed of user score similarity, user attribute feature similarity and user interest similarity, in which user registration information is used to extract attribute features to calculate user feature similarity; use the number of user evaluations of project attributes to measure users’ interest in different project attributes, use the similarity calculation formula to calculate the interest similarity value between users. The user attribute feature similarity and user interest similarity are combined with the user rating similarity to obtain the final similarity for recommendation. Finally, a simulation experiment is performed on the MovieLens movie data set. Through the experimental results, it can be seen that the improved collaborative filtering algorithm based on user characteristics and user interests not only solves the cold start problem, but also improves the recommendation accuracy.