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Improving recommendation lists through topic diversification
Abstract
In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.
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Cited by
Introduction to Recommender Systems Handbook
Ricci, F., Rokach, L., & Shapira, B. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_1 · Cited by 2,378
Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372850 · Cited by 1,233
Context-Aware Recommender Systems
Adomavicius, G., & Tuzhilin, A. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_7 · Cited by 1,102
Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., & He, X. (2023) · Proceedings of the 17th ACM Conference on Recommender Systems · doi:10.1145/3604915.3608857 · Cited by 469
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
Metadata
- Authors
- Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G.
- Venue
- Proceedings of the 14th international conference on World Wide Web - WWW '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 22
- OpenAlex
- W2155912844
- Cited by
- 1,892
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- closed
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- Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving recommendation lists through topic diversification. Proceedings of the 14th international conference on World Wide Web - WWW '05. https://doi.org/10.1145/1060745.1060754
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