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:Wang, Shuaiqiang
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
出版年:2014
卷:5
期:3
DOI:10.1145/2542048
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.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:8
Scopus被引频次:19
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907446973&doi=10.1145%2f2542048&partnerID=40&md5=e139d33c6f9782cac2ef61baf92282bb
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