一种基于Bhattacharyya系数和项目相关性的协同过滤算法

Collaborative Filtering Algorithm Based on Bhattacharyya Coefficient and Item Correlation

在大数据时代,为了满足用户的信息需求,个性化推荐系统得到了广泛应用。协同过滤是一种简单有效的推荐算法。然而,许多传统的相似度计算方法仅仅基于用户的共同评分值,且不适用于稀疏数据环境,因此提出了一种新的基于Bhattacharyya系数的相似度方法。该方法使用了所有用户对项目的评分信息,不仅可以通过用户的评分行为获得用户的相似兴趣特征,而且可以获得用户已评分物品之间的相关性;同时由于不同的用户有不同的评分习惯,新方法也考虑了每个用户的评分偏好。通过考虑用户相似性的更多因素,可以为目标用户选择更恰当的邻域用户,以更有效地提升推荐性能。在两个真实数据集上进行的实验表明,所提方法优于其他当前最好的相似度方法。

ln order to satisfy the inforτnation needs of users in the big data era,the personalized recommender system has been widely used.Collaborative filtering is a simple and effective recommendation algorithm.However,most traditional similarity methods only compute the similarity based on the users'co-rated scores.In addition,they are not very suitable in sparse data environment.This paper proposed a new similarity method based on Bhattacharyya coefficient.lt uses all users'rating information for items,which can not only obtain similar interest feature of users through the user'srating behavior,but also obtain the correlation between the items that the users have rated.Meanwhile,the new methodalso takes into account each user's rating preference,since different users have different rating habits.Considering more relevant factor about user similarity,more appropriate neighborhood can be selected for the target users,efficiently improving the recommendations.With experiments on two real data sets,the results show that our method outperforms the other state-of-the-art similarity metrics.