【摘要】 在推荐系统中,随时间精确捕获用户偏好能有效提高推荐精度。但基于所有用户的简单时间相关性通常是没有实际意义的,因为不同用户的偏好随着外部环境不同而发生改变。用户时下偏好受用户长期偏好和短期偏好的共同影响。为了捕获用户长期和短期偏好,在推荐系统中引入基于会话的时态图STG(Session-based Temporal Graph),提出基于STG的路径融合算法PFA(Path Fusion Algorithm),并生成对某个用户的Top-N物品推荐。使用CiteULike和Delicious两个历史数据集来评估算法的有效性,实验结果表明所提算法在准确度上要高于以往传统算法。更多还原

【Abstract】 Accurately capturing user preferences over time can improve the precision in recommender systems.Simple correlation over time is typically not meaningful,since users change their preferences due to different external events.User behavior can often be determined by individual’s long-term and short-term preferences.In order to capture it,we propose the Session-based Temporal Graph(STG).Based on the STG model framework,we propose the Path Fusion Algorithm(PFA)to make accurate top-N recommendation.And we evaluate the effectiveness of our method using two real datasets,CiteULike and Delicious.Experimental results show that the proposed algorithm has higher accuracy than most of previous algorithms.更多还原