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The long tail of recommender systems and how to leverage it
Abstract
The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.
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Citations
Cited by
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3418487 · Cited by 183
Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zhao, Z., Chen, J., Zhou, S., He, X., Cao, X., Zhang, F., & Wu, W. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3218994 · Cited by 117
“Worse still, recommendation model not only inherits the bias, but also amplifies bias, making the popular items dominate the top recommendations [13], [19], [20], [21], [22], [23].”
BackgroundBreak the Loop: Gender Imbalance in Music Recommenders
Ferraro, A., Serra, X., & Bauer, C. (2021) · Proceedings of the 2021 Conference on Human Information Interaction and Retrieval · doi:10.1145/3406522.3446033 · Cited by 92
Popularity Bias in Dynamic Recommendation
Zhu, Z., He, Y., Zhao, X., & Caverlee, J. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467376 · Cited by 82
Beyond Globally Optimal: Focused Learning for Improved Recommendations
Beutel, A., Chi, E. H., Cheng, Z., Pham, H., & Anderson, J. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052713 · Cited by 73
Cotter, K., Medeiros, M., Pak, C., & Thorson, K. (2021) · Big Data & Society · doi:10.1177/2053951721996046 · Cited by 63
Auditing Algorithmic Bias on Twitter
Bartley, N., Abeliuk, A., Ferrara, E., & Lerman, K. (2021) · 13th ACM Web Science Conference 2021 · doi:10.1145/3447535.3462491 · Cited by 62
Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives
Fu, Z., Niu, X., & Maher, M. L. (2024) · ACM Computing Surveys · doi:10.1145/3605145 · Cited by 30
Zhang, Z., Wang, Z., Ma, T., Taneja, V. S., Nelson, S., Le, N. H. L., Murugesan, K., Ju, M., Chawla, N. V., Zhang, C., et al. (2025) · Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 · doi:10.1145/3690624.3709382 · Cited by 16
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Huang, W.-C., Zhang, W., Liang, Y., Bei, Y., Chen, Y., Feng, T., Pan, X., Tan, Z., Wang, Y., Wei, T., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.06052 · Cited by 70
"Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data
Strauss, I., Yang, J., & Mazzucato, M. (2025) · arXiv
Metadata
- Authors
- Park, Y.-J., & Tuzhilin, A.
- Venue
- Proceedings of the 2008 ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2008-10-23
- Pages
- 11-18
- OpenAlex
- W2023954349
- Cited by
- 436
- References
- 0
- FWCI
- 7.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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