多因素复合度量的协同过滤推荐算法

Collaborative filtering recommendation algorithm measured by compound multiple factors

单一评分相似性度量及数据稀疏导致了传统推荐算法计算出的用户或项目近邻不准确、推荐质量不高,为此,提出了一种多因素复合度量的协同过滤推荐算法。该算法基于用户访问次数、停留时间及评分定义了一个多因素约束的相似性计算函数,避免了单一评分相似性度量问题,提高了相似性计算的准确度;同时,基于项目类别、目标用户已访问的项目、已访问过待预测评分项目的用户、访问时序建立了项目及用户信任模型,在数据稀疏及冷启动时用信任依赖度代替相似度预测评分,解决了相似性计算数据不充分的问题。实验结果表明,该算法能显著提高最近邻计算的准确性和算法的推荐质量。

Similarity used single mark and sparse data lead to inaccurate neighbors and poor recommender quality for the traditional recommendation algorithm. This paper proposed a collaborative filtering algorithm measured by compound multiple factors. The algorithm defined a similarity computing function constrained with visit times,access time,rating,which avoided the problem of similarity measured by single rating and improved the accuracy of similarity compute. To solve the problem of inade-quate data for computing similarity,the algorithm created item＇s and user trust model based on item category,items visited by target user,users who had visited target item,exploiting sequence and used trust degree instead of similar degree when data was very sparse. The experiment results show that the algorithm can significantly improve the neighbor accuracy and recommendation quality.