基于Jaccard相似度和位置行为的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on Jaccard Similarity and Locational Behaviors

协同过滤是现今推荐系统中应用最为成功且最广泛的推荐方法之一,其中概率矩阵分解算法作为一类重要的协同过滤方式,能够通过学习低维的近似矩阵进行推荐。然而,传统的协同过滤推荐算法在推荐过程中只利用用户-项目评分信息,忽略了用户(项目)间的潜在影响力,影响了推荐精度。针对上述问题,首先利用Jaccard相似度对用户(项目)做预处理,而后通过用户(项目)间的位置信息挖掘出其间的潜在影响力,成功找到最近邻居集合;最后将该邻居集合融合到基于概率矩阵分解的协同过滤推荐算法中。实验证明该算法较传统的协同过滤推荐算法能够更有效地预测用户的实际评分,提高了推荐效果。

Recently, collaborative filtering is one of the most widely used and successful recommendation technology in recommender system. And probabilistic matrix factorization is an important method of collaborative filtering and it can be recommended by learning the low dimensional approximation matrix. However, the traditional collaborative filtering recommendation algorithm has the disadvantages of using the ratings between users and items only, ignoring the potential impact of the users （items）. At last, it affects the recommendation precision. In order to solve the problem, in this paper,we first used the Jaccard similarity to preprocess the users （items）, and then dug out the potential impact through the users （items） location information, finding the set of nearest neighbors successfully. Furthermore, those nearest neighbors were successfully applied into the recommendation process based on probabilistic matrix factorization- Experimental results show that compared to traditional collaborative filtering recommendation algorithm, the proposed algorithm can achieve more accurate rating predictions and improve the quality of recommendation.