Abstract: Concerning the poor quality of recommendations of traditional user-based and item-based collaborative filtering models, a new collaborative filtering model combining users and items predictions was proposed. Firstly, it considered both users and items, and optimized the similarity model with excellent performance dynamically. Secondly, it constructed neighbor sets for the target objects by selecting some similar users and items according to the similarity values, and then obtained the user-based and item-based prediction results respectively based on some prediction functions. Finally, it gained final predictions by using the adaptive balance factor to coordinate both of the prediction results. Comparative experiments were carried out under different evaluation criteria, and the results show that, compared with some typical collaborative filtering models such as RSCF, HCFR and UNCF, the proposed model not only has better performance in prediction accuracy of items, but also does well in the precision and recall of recommendations.

Key words: recommender system, collaborative filtering, neighbor set, similarity model, Mean Absolute Error (MAE)

摘要： 针对基于用户和基于项目的协同过滤模型存在推荐质量不高等问题，提出一种综合用户和项目预测的协同过滤模型。该模型同时考虑用户和项目两方面，首先对性能优秀的相似性模型进行自适应的优化;然后根据相似性值分别选取相似用户和相似项目为目标对象构造近邻集合，并利用预测函数得到基于用户和基于项目的预测结果;最后通过自适应平衡因子的协调处理获得最终预测结果。比较实验在不同的评估标准下进行,结果表明，与目前典型的模型如RSCF、HCFR和UNCF相比，新提出的协同过滤模型不仅在项目预测准确性方面拥有出色的表现，而且在推荐准确性和全面性方面同样表现优秀。

关键词: 推荐系统, 协同过滤, 近邻集合, 相似性模型, 平均绝对偏差