원문정보
초록
영어
As an important tool to help users filter Internet information, recommender system has played a very important role wherever in academia or in industrial area. During the past years, different recommendation approaches based on the social network have been proposed with the rapid development of online social networks. Different from the traditional ones which assume all the users are independent and identically distributed, these approaches follow the intuition that a person’s implicit or explicit social network will affect his behaviors on the Web. In this paper, on the basis of the existing work, we fuse a baseline predictor model with an improved social recommendation model and propose a social recommendation algorithm based on probability matrix factorization. The experimental result shows that our method outperforms the existing approaches in accuracy.
목차
1. Introduction
2. Related Work
3. Problem Description
4. Social Recommendation Algorithm
4.1. BPMF Model
4.2. ISMF Model
4.3. BISMF Model
4.4. Similarity Function
4.5. Complexity Analysis
5. Results and Analysis
5.1. Experimental Environment and Data Set
5.2. Evaluation Index
5.3. Experimental Results
5.4. Influence of θc
6. Conclusion
Acknowledgment
References