标题：VSRank: A Novel Framework for Ranking-Based Collaborative Filtering
作者：Wang, Shuaiqiang; Sun, Jiankai; Gao, Byron J.; Ma, Jun
作者机构：[Wang, Shuaiqiang] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Peoples R China.; [Sun, Jiankai] Shandong Univ, Sch Comp Sci 更多
通讯作者地址：[Wang, SQ]Shandong Univ Finance & Econ, Sch Comp Sci & Technol, 7366 East 2nd Ring Rd, Jinan 250014, Peoples R China.
来源：ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
关键词：Algorithms; Performance; Experimentation; Recommender systems;; collaborative filtering; ranking-based collaborative filtering; vector; space model; term weighting
摘要：Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.