The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
EigenRank: a ranking-oriented approach to collaborative filtering
Abstract
A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a database of users' past ratings on a set of items, traditional collaborative filtering algorithms are based on predicting the potential ratings that a user would assign to the unrated items so that they can be ranked by the predicted ratings to produce a list of recommended items. In this paper, we propose a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings. We measure the similarity between users based on the correlation between their rankings of the items rather than the rating values and propose new collaborative filtering algorithms for ranking items based on the preferences of similar users. Experimental results on real world movie rating data sets show that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836
Haveliwala, T. H. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511513 · Cited by 1,567
Bianchini, M., Gori, M., & Scarselli, F. (2005) · ACM Transactions on Internet Technology · doi:10.1145/1052934.1052938 · Cited by 539
Cited by
Recommender systems with social regularization
Ma, H., Zhou, D., Liu, C., Lyu, M. R., & King, I. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935877 · Cited by 1,626
QoS Ranking Prediction for Cloud Services
Zheng, Z., Wu, X., Zhang, Y., Lyu, M. R., & Wang, J. (2013) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.285 · Cited by 309
Collaborative personalized tweet recommendation
Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., & Yu, Y. (2012) · Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2348283.2348372 · Cited by 296
Optimizing top-n collaborative filtering via dynamic negative item sampling
Zhang, W., Chen, T., Wang, J., & Yu, Y. (2013) · Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2484028.2484126 · Cited by 256
Interactive collaborative filtering
Zhao, X., Zhang, W., & Wang, J. (2013) · Proceedings of the 22nd ACM international conference on Information & Knowledge Management · doi:10.1145/2505515.2505690 · Cited by 165
Metadata
- Authors
- Liu, N. N., & Yang, Q.
- Venue
- Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2008-07-20
- Pages
- 83-90
- OpenAlex
- W2134716712
- Cited by
- 249
- References
- 7
- FWCI
- 11.28
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, N. N., & Yang, Q. (2008). EigenRank: a ranking-oriented approach to collaborative filtering. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1390334.1390351
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
