The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
Abstract
Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.
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Cites
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Cited by
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
Rank and relevance in novelty and diversity metrics for recommender systems
Vargas, S., & Castells, P. (2011) · Proceedings of the fifth ACM conference on Recommender systems · doi:10.1145/2043932.2043955 · Cited by 709
DRN: A Deep Reinforcement Learning Framework for News Recommendation
Zheng, G., Zhang, F., Zheng, Z., Xiang, Y., Yuan, N. J., Xie, X., & Li, Z. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3185994 · Cited by 630
Luo, X., Zhou, M., Xia, Y., & Zhu, Q. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2308433 · Cited by 629
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
Metadata
- Authors
- Adomavicius, G., & Kwon, Y.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2012-05
- Volume
- 24
- Issue
- 5
- Pages
- 896-911
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2153578526
- Cited by
- 675
- References
- 48
- FWCI
- 96.18
- Type
- journal article
- Language
- en
- Open access
- closed
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