基于核方法的User-Based协同过滤推荐算法

A Kernel and User-Based Collaborative Filtering Recommendation Algorithm

作为在实际系统中运用最为广泛和成功的推荐技术,协同过滤算法得到了研究者们的广泛关注.传统的协同过滤算法面临着数据稀疏和冷启动等问题的挑战,在计算用户之间相似度时只能考虑有限的数据,因此难以对用户之间的相似度进行准确的估计.提出了一种基于核密度估计的用户兴趣估计模型,并基于此模型,提出了一种基于核方法的user-based协同过滤推荐算法.通过挖掘用户在有限的评分数据上表现出来的潜在兴趣,该算法能更好地描述用户兴趣在项目空间上的分布,进而可以更好地估计用户之间的兴趣相似度.实验表明,该算法可以有效地提高推荐系统的性能,尤其在数据稀疏的情况下能显著地提高推荐结果的质量.

With the development of information technology, people can get more and more information nowadays. To help users find the information that meets their needs or interest among large amount of data, personalized recommendation technology has emerged and flourished. As a most widely used and successful recommendation technique, collaborative filtering algorithm has widely spread and concerned many researchers. Traditional collaborative filtering algorithms face data sparseness and cold start problems. As traditional algorithms only consider the limited data, it is difficult to estimate the accurate similarity between users, as well as the final recommendation results. This paper presents a kernel-density-estimation-based user interest model, and based on this model, a user-based collaborative recommendation algorithm based on kernel method is proposed. Through mining users＇ latent interest suggested by the limited ratings, the algorithm can well estimate the distribution of users＇ interest in the item space, and provide a better user similarity calculation method. A distance measurement based on classification similarity is proposed for the kernel methods, and two kernel functions are investigated to estimate the distribution of user interest. KL divergence is utilized to measure the similarity of users＇ interest distribution. Experiments show that the algorithm can effectively improve the performance of the recommendation system, especially in the case of sparse data.