中文摘要：

作为目前构建推荐系统最成功的方法之一，协同过滤算法（CF）是利用已知的一组用户喜好数据来预测用户对其他物品的喜好从而做出个性化推荐的。两种比较成功的协同过滤算法能够直接刻画用户和物品因子的隐语义模型，以及分析物品或者用户之间相似度的邻域模型。提出了一种针对这两种模型的改进方法，使得隐语义模型和邻域模型能够有效结合，从而构建出一个更精确的融合模型。在融合用户的显性反馈与隐性反馈信息对模型进行扩展后，又使得精确度进一步提升。在Netflix数据集上进行测试，实验结果表明，该融合算法在Netflix数据集上的性能优于其他算法。

英文摘要：

As one of the most successful approaches to building recommender systems, Collaborative Filtering（CF）uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. The two successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. This paper introduces some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data, and the results are better than those previously published on that dataset.