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题名Diversified and Scalable Service Recommendation with Accuracy Guarantee
作者
发表日期2021-10-01
发表期刊IEEE Transactions on Computational Social Systems
ISSN/eISSN2329-924X
卷号8期号:5页码:1182-1193
摘要

As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user-item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users' requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users' satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability.

关键词Accuracy collaborative filtering (CF) diversity recommendation scalability
DOI10.1109/TCSS.2020.3007812
URL查看来源
收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Cybernetics ; Computer Science, Information Systems
WOS记录号WOS:000702557700016
Scopus入藏号2-s2.0-85089297705
引用统计
被引频次:32[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/7031
专题个人在本单位外知识产出
通讯作者Qi, Lianyong
作者单位
1.School of Information Science and Engineering, Qufu Normal University, Rizhao, China
2.Department of Computing, Macquarie University, Sydney, Australia
3.College of Computer Science and Technology, Huaqiao University, Quanzhou, China
4.School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
5.Department of Mathematics and Computer Science, Brandon University, Brandon, Canada
6.School of Science, Engineering and Information Technology, Federation University, Ballarat, Australia
7.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China
推荐引用方式
GB/T 7714
Wang, Lina,Zhang, Xuyun,Wang, Tianet al. Diversified and Scalable Service Recommendation with Accuracy Guarantee[J]. IEEE Transactions on Computational Social Systems, 2021, 8(5): 1182-1193.
APA Wang, Lina., Zhang, Xuyun., Wang, Tian., Wan, Shaohua., Srivastava, Gautam., .. & Qi, Lianyong. (2021). Diversified and Scalable Service Recommendation with Accuracy Guarantee. IEEE Transactions on Computational Social Systems, 8(5), 1182-1193.
MLA Wang, Lina,et al."Diversified and Scalable Service Recommendation with Accuracy Guarantee". IEEE Transactions on Computational Social Systems 8.5(2021): 1182-1193.
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