[Objective] This paper tries to improve the performance of traditional collaborative filtering and recommendation algorithm. [Methods] We used the MovieLens dataset to evaluate the proposed algorithm. First, chose datasets with sparse degree of 0.9605, which included scoring records of 1,102 users for 2,920 movies. Second, identified the optimal number of expert users and recommended weight coefficient alpha value with series of experiments. Finally, evaluated the algorithm’s performance with comparative method. [Results] The precision of the algorithm were influenced by the expert users. When the recommended weight coefficient value was 0.6, the precision of the new algorithm was better than the traditional ones. Once the propotion of expert users increased from 2% to 20%, the coverage value increased by 0.21. Thus, the new algorithm could analyze the long tail goods more effectively. [Limitations] We did not take into account the possible correlation among different categories. [Conclusions] The proposed algorithm could effectively solve the data sparsity and cold start issues, which significantly improve the performance of the recommendation system.
谭学清,张磊,黄翠翠,罗琳. 融合领域专家信任与相似度的协同过滤推荐算法研究*[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2016.07.13.
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