考虑用户兴趣变化的概率隐语意协同推荐算法

推荐系统是人们从海量信息中获取对自己有用信息的一种有效途径，在学术界和工业界都受到广泛关注. 协同过滤则是推荐系统领域最流行的算法，目前很多协同过滤算法都是静态模型，没有考虑到用户兴趣会随着时间而变化. 本文提出一种融合算法，利用高斯概率隐语意（PLSA）模型提取出用户的长期兴趣分布，然后结合用户评分时间窗捕获用户短期兴趣变化，从而更准确的为用户做出推荐. 在Netflix和MovieLens数据集的上测试表明，改进算法的预测评分准确率明显高于经典的基于用户相似度算法和PLSA算法.

PLSA Collaborative Filtering Algorithm Incorporated with User Interest Change

Recommend system is an effective method for people to get useful knowledge from mass information. It has attracted widespread attention in both academia and industry. Collaborative filtering (CF) is the most popular algorithm in the research of Recommend system. However most of current CF algorithms are static models, which do not take into account of user interest changing. The paper proposed a hybrid recommend method, which capture user's long-term interests with Gaussian probabilistic latent semantic (PLSA) algorithm, at the same time, capture user's short-time interests with rating window. The experimental results obtained on Movielens dataset and Netflix dataset clearly show that the new algorithm is more accurate than traditional user-based algorithm and PLSA algorithm.