research-article

Collaborative ranking

Authors:

Suhrid Balakrishnan,

Sumit ChopraAuthors Info & Claims

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

Pages 143 - 152

https://doi.org/10.1145/2124295.2124314

Published: 08 February 2012 Publication History

Abstract

Typical recommender systems use the root mean squared error (RMSE) between the predicted and actual ratings as the evaluation metric. We argue that RMSE is not an optimal choice for this task, especially when we will only recommend a few (top) items to any user. Instead, we propose using a ranking metric, namely normalized discounted cumulative gain (NDCG), as a better evaluation metric for this task. Borrowing ideas from the learning to rank community for web search, we propose novel models which approximately optimize NDCG for the recommendation task. Our models are essentially variations on matrix factorization models where we also additionally learn the features associated with the users and the items for the ranking task. Experimental results on a number of standard collaborative filtering data sets validate our claims. The results also show the accuracy and efficiency of our models and the benefits of learning features for ranking.

References

[1]

Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. A neural probabilistic language model. Journal of Machine Learning Research, 3:1137--1155, 2003.

Digital Library

[2]

L. Breiman. Random forests. In Machine Learning, volume 45(1), 2001.

Digital Library

[3]

C. Burges. From ranknet to lambdarank to lambdamart: An overview. In Microsoft Research Technical Report MSR-TR-2010-82, 2010.

[4]

R. Collobert and J. Weston. A unified architecture for natural language processing: deep neural networks with multitask learning. In ICML '08: Proceedings of the 25th international conference on Machine learning, pages 160--167, New York, NY, USA, 2008. ACM.

Digital Library

[5]

D. Cossock and T. Zhang. Statistical analysis of bayes optimal subset ranking. IEEE Transactions on Information Theory, 54(11):5140--5154, 2008.

Digital Library

[6]

P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pages 39--46, New York, NY, USA, 2010. ACM.

Digital Library

[7]

P. Donmez, K. M. Svore, and C. J. Burges. On the local optimality of lambdarank. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pages 460--467, New York, NY, USA, 2009. ACM.

Digital Library

[8]

Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 4:933--969, 2003.

Digital Library

[9]

J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189--1232, 2000.

[10]

S. Hacker and L. von Ahn. Matchin: eliciting user preferences with an online game. In CHI '09: Proceedings of the 27th international conference on Human factors in computing systems, pages 1207--1216, New York, NY, USA, 2009. ACM.

Digital Library

[11]

R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In P. J. Bartlett, B. Schölkopf, D. Schuurmans, and A. J. Smola, editors, Advances in Large Margin Classifiers, pages 115--132. MIT Press, 2000.

[12]

K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst., 20:422--446, October 2002.

Digital Library

[13]

T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '02, pages 133--142, New York, NY, USA, 2002. ACM.

Digital Library

[14]

Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. Computer, 42(8):30--37, 2009.

Digital Library

[15]

P. Li, C. J. C. Burges, and Q. Wu. Mcrank: Learning to rank using multiple classification and gradient boosting, 2007.

[16]

B. M. Marlin and R. S. Zemel. Collaborative prediction and ranking with non-random missing data. Proceedings of the third ACM conference on Recommender systems (RecSys '09), 2009.

Digital Library

[17]

P. Melville and V. Sindhwani. Recommender Systems. 2010.

[18]

A. Mohan, Z. Chen, and K. Q. Weinberger. Web-search ranking with initialized gradient boosted regression trees. Journal of Machine Learning Research, Workshop and Conference Proceedings, 14:77--89, 2011.

[19]

M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web, WWW '07, pages 521--530, New York, NY, USA, 2007. ACM.

Digital Library

[20]

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Neural Information Processing Systems (NIPS), 2007.

[21]

H. Steck and R. S. Zemel. A generalized probabilistic framework and its variants for training top-k recommender systems. Workshop on the Practical Use of Recommender Systems, Algorithms and Technologies (PRSAT 2010) in conjunction with RecSys 2010, 2010.

[22]

M. Weimer, A. Karatzoglou, Q. V. Le, and A. Smola. Cofi-rank: Maximum margin matrix factorization for collaborative ranking. In Neural Information Processing Systems (NIPS), 2007.

[23]

J. Xu and H. Li. Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, pages 391--398, New York, NY, USA, 2007. ACM.

Digital Library

[24]

J. Xu, T.-Y. Liu, M. Lu, H. Li, and W.-Y. Ma. Directly optimizing evaluation measures in learning to rank. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pages 107--114, New York, NY, USA, 2008. ACM.

Digital Library

Cited By

Bharti MPradhan GRout D(2024)Centroid Based Clustering Models for Designing Dense Ranking of Schools Focusing on Total Marks of Individual Students in a Common Examination2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)10.1109/IC-CGU58078.2024.10530803(1-6)Online publication date: 1-Mar-2024
https://doi.org/10.1109/IC-CGU58078.2024.10530803
Wang CZhu HZhu CQin CChen EXiong H(2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1145/3626194
Lu XWu JYuan JChen HDuh WHuang HKato MMothe JPoblete B(2023)Optimizing Reciprocal Rank with Bayesian Average for improved Next Item RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592033(2236-2240)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592033
Show More Cited By

Index Terms

Collaborative ranking
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

February 2012

792 pages

ISBN:9781450307475

DOI:10.1145/2124295

General Chairs:
Eytan Adar
University of Michigan, USA
,
Jaime Teevan
Microsoft Research, USA
,
Program Chairs:
Eugene Agichtein
Emory University, USA
,
Yoelle Maarek
Yahoo! Research, Israel

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 February 2012

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WSDM'12

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WSDM'12: Fifth ACM International Conference on Web Search and Data Mining

February 8 - 12, 2012

Washington, Seattle, USA

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Cited By

Bharti MPradhan GRout D(2024)Centroid Based Clustering Models for Designing Dense Ranking of Schools Focusing on Total Marks of Individual Students in a Common Examination2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)10.1109/IC-CGU58078.2024.10530803(1-6)Online publication date: 1-Mar-2024
https://doi.org/10.1109/IC-CGU58078.2024.10530803
Wang CZhu HZhu CQin CChen EXiong H(2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1145/3626194
Lu XWu JYuan JChen HDuh WHuang HKato MMothe JPoblete B(2023)Optimizing Reciprocal Rank with Bayesian Average for improved Next Item RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592033(2236-2240)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592033
P SP BD ST E RS AK K(2023)Movie Endorsement to Deliver Top-N Recommendation for User using CoFiTor Framework2023 8th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES57224.2023.10192677(1699-1703)Online publication date: 1-Jun-2023
https://doi.org/10.1109/ICCES57224.2023.10192677
Riofrío JRecalde LNavarrete R(2023)Profit vs Accuracy: Balancing the Impact on Users Introduced by Profit-Aware Recommender SystemsInformation and Communication Technologies10.1007/978-3-031-45438-7_12(175-192)Online publication date: 6-Oct-2023
https://doi.org/10.1007/978-3-031-45438-7_12
Wang WChai YLi Y(2022)A Global to Local Guiding Network for Missing Data ImputationICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP43922.2022.9746136(4058-4062)Online publication date: 23-May-2022
https://doi.org/10.1109/ICASSP43922.2022.9746136
Marcuzzo MZangari AAlbarelli AGasparetto A(2022)Recommendation Systems: An Insight Into Current Development and Future Research ChallengesIEEE Access10.1109/ACCESS.2022.319453610(86578-86623)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3194536
Jiang RFeng SZhang SLi XYao YZhang H(2022)A personalized recommendation method based on collaborative ranking with random walkMultimedia Tools and Applications10.1007/s11042-022-11980-7Online publication date: 26-Jan-2022
https://doi.org/10.1007/s11042-022-11980-7
Wang WChai YLi Y(2022)GAGIN: generative adversarial guider imputation network for missing dataNeural Computing and Applications10.1007/s00521-021-06862-234:10(7597-7610)Online publication date: 8-Jan-2022
https://doi.org/10.1007/s00521-021-06862-2
Sharma SShakya H(2022)An Efficient Hybrid Recommendation Model with Deep Neural NetworksMachine Intelligence and Smart Systems10.1007/978-981-16-9650-3_36(463-472)Online publication date: 24-May-2022
https://doi.org/10.1007/978-981-16-9650-3_36
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