用户情境下基于信息增益和项目的协同过滤推荐技术研究

摘要

情境对于推荐系统的重要性已经得到了众多学者的普遍认可。然而在现有的基于情境感知的推荐中，基本上赋予所有情境因素的权重都是一样的，这种权重等分的做法很大程度上影响了推荐结果的质量。因此，提出了一种用户情境下基于信息增益和项目的协同过滤推荐技术，运用信息增益理论对诸多情境因素进行属性约简，计算不同情境属性的权重，抽取出对推荐结果影响较大的重要情境信息，将其与传统的基于项目的协同过滤推荐算法相结合，为处于特定情境下的用户提供个性化推荐。最后，通过实验证明该技术可以有效地提高推荐结果的准确率。关键词个性化推荐情境感知信息增益属性约简协同过滤。%The importance of context in recommender systems has gained general recognition of the numerous scholars. However, in the existing context-aware recommendation, the weights of all the contextual factors are basically the same, which largely limits the quality of the recommended results. Therefore, this paper proposes a context-aware recommendation technology based on information gain and item-based collaborative filtering, applies the theory of information gain to reduce contextual factors, calculates the weights of different contex-tual attributes and extracts the significant contextual information that influences the recommended results greatly. The combination with the traditional item-based collaborative filtering algorithm provides appropriate items to specific users under particular contexts. Finally, the experiment shows that the proposed approach is helpful to improve the accuracy of recommended results.