Abstract
Recommendation system is a very important tool to help users to find what they are interested in on the web. In many commercial recommendation systems, only the top-K items are shown to users, and recommendation becomes a ranking task rather than a classical rating prediction task. In this paper, we propose a new collaborative ranking algorithm based on learning to rank framework in information retrieval. For a given user-item pair (u,i), we use Kendall Rank Coefficient as similarity metric to choose neighborhood for user u and use the ranking statistical information of item i from user u’s neighborhood as the feature representation of pair (u,i). We apply LambdaRank to learn the ranking model and experimentally demonstrate the effectiveness of our method by comparing its performance with several collaborative ranking approaches. Moreover, we can address scenarios where users’ feedbacks are non-numerical scores.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Koren, Y.: Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data 4(1), 1:1–1:24 (2010)
Liu, T.Y.: Learning to rank for information retrieval, Berlin, German, vol. 3(3), pp. 225–331. Springer (2011)
Volkovs, M.N., Zemel, R.S.: Collaborative ranking with 17 parameters. In: Proceedings of the Twenty-sixth Annual Conference on Neural Information Processing Systems, NIPS 2012, Lake Tahoe, Nevada, United States, December 3-6, MIT Press (2013)
Balakrishnan, S., Chopra, S.: Collaborative ranking. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 143–152. ACM, New York (2012)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
Liu, N.N., Yang, Q.: Eigenrank: a ranking-oriented approach to collaborative filtering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 83–90. ACM, New York (2008)
Marden, J.I.: Analyzing and modeling rank data. Chapman & Hall Press, New York (1995)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46. ACM, New York (2010)
Koren, Y., Sill, J.: Ordrec: an ordinal model for predicting personalized item rating distributions. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 117–124. ACM, New York (2011)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, Arlington, Virginia, United States, pp. 452–461. AUAI Press (2009)
Shi, Y., Larson, M., Hanjalic, A.: List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 269–272. ACM, New York (2010)
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., Oliver, N.: Tfmap: optimizing map for top-n context-aware recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 155–164. ACM, New York (2012)
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., Hanjalic, A.: Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 139–146. ACM, New York (2012)
Weimer, M., Karatzoglou, A., Bruch, M.: Maximum margin matrix factorization for code recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys 2009, pp. 309–312. ACM, New York (2009)
Burges, C.J.C., Ragno, R., Le, Q.V.: Learning to rank with nonsmooth cost functions. In: Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, NIPS 2006, Vancouver, British Columbia, Canada, December 4-7, pp. 193–200. MIT Press (2007)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fan, C., Lin, Z. (2013). Collaborative Ranking with Ranking-Based Neighborhood. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_74
Download citation
DOI: https://doi.org/10.1007/978-3-642-37401-2_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37400-5
Online ISBN: 978-3-642-37401-2
eBook Packages: Computer ScienceComputer Science (R0)