摘要： 基于项目的协同过滤算法仅通过计算项目相似性产生推荐结果，忽略了项目类别信息对项目相似性的影响，且未考虑时间因素对推荐结果产生的影响。针对上述问题，引入项目类别相似性、用户兴趣度时间加权函数和项目流行度时间加权函数，提出结合项目类别相似性和动态时间加权的协同过滤推荐算法，包括将项目类别相似性引入到传统项目相似性计算中。分析用户兴趣度和项目受欢迎程度随时间动态变化对推荐结果产生的影响，构造基于时间的用户兴趣度加权函数和基于时间的项目流行度加权函数。实验结果表明，该算法的项目类别特征能够进一步提高项目相似性的精度，动态时间加权函数能够及时反映用户兴趣度和项目受欢迎程度的变化，提高推荐的准确度。

关键词: 推荐系统, 协同过滤, 项目相似性, 项目类别, 时间加权

Abstract: When searching the nearest neighbor set, the traditional item-based collaborative filtering algorithm only takes into account the similarity between the items, which ignores the impact of the items categories similarity and the time factor on recommendation. Aiming at the above problems, an improved item-based collaborative filtering algorithm combining items categories similarity and dynamic time weight is proposed. In this algorithm, the items category similarity is introduced to improve the accuracy of the similarity between items, and two kinds of weighting functions are constructed to incorporate temporal information into the prediction algorithm so as to adapt to changes in both user and item characteristics over time. Experimental results show that the proposed algorithm can efficiently trace the drifting of users’ interests and items’ popularity and improve the accuracy of the prediction.

Key words: recommendation system, collaborative filtering, item similarity, item category, time weighting

中图分类号: 

• TP18