Ranking-Oriented Collaborative Filtering: A Listwise Approach
Ranking-Oriented Collaborative Filtering: A Listwise Approach
复制标题
面向排名的协同过滤：列表方法
DOI：
10.1145/2960408
复制
发表时间：
2016-12-01
影响因子：
5.6
通讯作者：
Veijalainen, Jari
中科院分区：
文献类型：
--
作者：
Wang, Shuaiqiang;Huang, Shanshan;Veijalainen, Jari
协同过滤是推荐系统中最有效的技术之一，它既可以是面向评级的，也可以是面向排名的。面向排名的CF算法在排名准确性方面表现出显著的性能提升，能够为每个用户估计项目的精确偏好排名，而不是绝对评级(如面向评级的CF算法所做的那样)。传统的基于存储器的面向排序的CF可以被称为成对算法。它们将每个用户表示为每对项目的一组偏好，以进行相似性计算和预测。在这项研究中，我们提出了ListCF，这是一种新的列表模式，与成对模式相比，它寻求在准确率和效率上的改进。在ListCF中，每个用户被表示为基于Plackett-Luce模型的评级项目上的排列的概率分布，并且用户之间的相似性基于他们在共同评级项目集合上的概率分布之间的Kullback-Leibler散度来度量。在给定目标用户和最相似用户的情况下，ListCF基于相似用户在项目排列上的概率分布直接预测每个用户的项目的总顺序。此外，我们还从矩阵表示的角度揭示了逐点、成对和按列表的CF算法之间的内在联系。此外，为了提高算法的可扩展性和自适应性，我们提出了一种增量式ListCF算法，允许在用户提交新的评分或更新现有评分时增量地更新用户之间的相似度。在基准数据集上与最先进的方法进行了大量的实验，证明了我们方法的前景。
Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in both accuracy and efficiency in comparison with pairwise CF. In ListCF, each user is represented as a probability distribution of the permutations over rated items based on the Plackett-Luce model, and the similarity between users is measured based on the Kullback-Leibler divergence between their probability distributions over the set of commonly rated items. Given a target user and the most similar users, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items. Besides, we also reveal insightful connections among pointwise, pairwise, and listwise CF algorithms from the perspective of the matrix representations. In addition, to make our algorithm more scalable and adaptive, we present an incremental algorithm for ListCF, which allows incrementally updating the similarities between users when certain user submits a new rating or updates an existing rating. Extensive experiments on benchmark datasets in comparison with the state-of-the-art approaches demonstrate the promise of our approach.