The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Optimizing top-n collaborative filtering via dynamic negative item sampling
Abstract
Collaborative filtering techniques rely on aggregated user preference data to make personalized predictions. In many cases, users are reluctant to explicitly express their preferences and many recommender systems have to infer them from implicit user behaviors, such as clicking a link in a webpage or playing a music track. The clicks and the plays are good for indicating the items a user liked (i.e., positive training examples), but the items a user did not like (negative training examples) are not directly observed. Previous approaches either randomly pick negative training samples from unseen items or incorporate some heuristics into the learning model, leading to a biased solution and a prolonged training period. In this paper, we propose to dynamically choose negative training samples from the ranked list produced by the current prediction model and iteratively update our model. The experiments conducted on three large-scale datasets show that our approach not only reduces the training time, but also leads to significant performance gains.
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Cites
BPR: Bayesian Personalized Ranking from Implicit Feedback
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,242
One-Class Collaborative Filtering
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.16 · Cited by 1,015
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COFI RANK - Maximum margin matrix factorization for collaborative ranking
Weimer, M., Karatzoglou, A., Le, Q. V., & Smola, A. J. (2007) · HAL · Cited by 356
EigenRank: a ranking-oriented approach to collaborative filtering
Liu, N. N., & Yang, Q. (2008) · Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1390334.1390351 · Cited by 249
List-wise learning to rank with matrix factorization for collaborative filtering
Shi, Y., Larson, M., & Hanjalic, A. (2010) · Proceedings of the fourth ACM conference on Recommender systems · doi:10.1145/1864708.1864764 · Cited by 246
TFMAP: optimizing MAP for top-n context-aware recommendation
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., & Oliver, N. (2012) · Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2348283.2348308 · Cited by 189
Local implicit feedback mining for music recommendation
Yang, D., Chen, T., Zhang, W., Lu, Q., & Yu, Y. H. (2012) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2365952.2365973 · Cited by 44
Self-citationSerendipitous Personalized Ranking for Top-N Recommendation
Lu, Q., Chen, T., Zhang, W., Yang, D., & Yu, Y. H. (2012) · doi:10.5555/2457524.2457692 · Cited by 41
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IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Wang, J., Yu, L., Zhang, W., Gong, Y., Xu, Y., Wang, B., Zhang, P., & Zhang, D. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080786 · Cited by 567
Self-citationOptimal real-time bidding for display advertising
Zhang, W., Yuan, S., & Wang, J. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623633 · Cited by 305
Self-citationBootstrap Latent Representations for Multi-modal Recommendation
Zhou, X., Zhou, H., Liu, Y., Zeng, Z., Miao, C., Wang, P., You, Y., & Jiang, F. (2023) · Proceedings of the ACM Web Conference 2023 · doi:10.1145/3543507.3583251 · Cited by 298
Understanding Negative Sampling in Graph Representation Learning
Yang, Z., Ding, M., Zhou, C., Yang, H., Zhou, J., & Tang, J. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403218 · Cited by 159
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
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Metadata
- Authors
- Zhang, W., Chen, T., Wang, J., & Yu, Y.
- Venue
- Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2013-07-28
- Pages
- 785-788
- OpenAlex
- W2102035799
- Cited by
- 256
- References
- 9
- FWCI
- 3.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, W., Chen, T., Wang, J., & Yu, Y. (2013). Optimizing top-n collaborative filtering via dynamic negative item sampling. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/2484028.2484126
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