- Title
- Diversified and scalable service recommendation with accuracy guarantee
- Creator
- Wang, Lina; Zhang, Xuyun; Wang, Tian; Wan, Shaohua; Pang, Shaoning
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179087
- Identifier
- vital:15528
- Identifier
-
https://doi.org/10.1109/TCSS.2020.3007812
- Identifier
- ISBN:2329-924X (ISSN)
- Abstract
- 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. © 2014 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Computational Social Systems Vol. 8, no. 5 (2021), p. 1182-1193
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2020 IEEE
- Subject
- 0906 Electrical and Electronic Engineering; Accuracy; collaborative filtering (CF); diversity; recommendation; scalability
- Reviewed
- Funder
- This work was supported in part by the National Natural Science Foundation of China under Grant 61872219, in part by the Natural Science Foundation of Shandong Province under Grant ZR2019MF001, and in part by the Open Project of State Key Laboratory for Novel Software Technology under Grant KFKT2020B08.
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