关键词: 协同过滤, Sigmoid函数, 数据稀疏性, 推荐系统, 用户兴趣

Abstract:

Collaborative filtering techniques have been widely used in various recommender systems. However, they are likely to suffer from the data sparsity problem which often results in inaccurate recommendation results. In view of this problem, this paper proposes a collaborative filtering recommendation algorithm based on Sigmoid function, by analyzing users' invocation records of services. Firstly, the relationship between a user and the invocation number of service is analyzed to acquire the user's interest in contents of service by using TF-IDF algorithm. Then, a Sigmoid function is used to calculate the user's interest in function of service according to the invocation number. Finally, personalized service recommendation is performed by combining the user's contents interestingness with the function interestingness. The experimental results show that this algorithm can effectively alleviate the data sparsity problem and thus achieve better prediction accuracy.

Key words: collaborative filtering, Sigmoid function, data sparsity, recommender system, users?? interest