基于有效特征子集提取的高效推荐算法

推荐系统是根据用户的历史信息对未知信息进行预测.用户项目评分矩阵的稀疏性是目前推荐系统面临的主要瓶颈之一.跨域推荐系统是解决数据稀疏性问题的一种有效方法.本文提出了基于有效特征子集选取的高效推荐算法（FSERA），FSERA是提取辅助域的子集信息，来扩展目标域数据，从而对目标域进行协同过滤推荐.本文采用K-means聚类算法将辅助域的数据进行提取来降低冗余和噪声，获取了辅助域的有效子集，不仅降低了算法复杂度，而且扩展了目标域数据，提高了推荐精度.实验表明，此方法比传统的方法有更高的推荐精度.

Efficient Recommendation Algorithm Based on Feature Subset Extraction

The recommendation system predicts the unknown information according to the user''s historical information. Sparsity of user item scoring matrix is one of the main bottlenecks faced by recommendation system. Cross-domain recommendation system is an effective method to solve the problem of data sparsity. In this study, an Efficient Recommendation Algorithm based on effective Feature Subset selection (FSERA) is proposed. FSERA extracts subset information of auxiliary domain to expand target domain data, so as to collaboratively filter recommendation for target domain. In this study, K-means clustering algorithm is used to extract data from the auxiliary domain to reduce redundancy and noise, and to obtain an effective subset of the auxiliary domain, which not only reduces the complexity of the algorithm, but also expands the target domain data and improves the recommendation accuracy. Experiments show that this method has higher recommendation accuracy than traditional methods.