发表状态 | 已发表Published |
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
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ISSN/eISSN | 2329-924X |
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 |
DOI | 10.1109/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 | |
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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