JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (5): 29-36.doi: 10.6040/j.issn.1672-3961.2.2015.008

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A local collaborative filtering algorithm based on ranking recommendation tasks

HUANG Dan, WANG Zhihai, LIU Haiyang   

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2015-05-16 Online:2016-10-20 Published:2015-05-16

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Abstract: Based on matrix factorization model, time factor and ranking problem, a collaborative filtering algorithm was proposed. The method relaxed the low-rank assumption of rating matrix and assumed that the rating matrix was locally low-rank,which meaned that the rating matrix was low-rank in the neighborhood of certain user-item combination. Mean reciprocal rank(MRR), an evaluation metric widely used in Information retrieval, was modified to fit the rating dataset. The evaluation metric was smoothed and simplied, and then was optimized. The algorithm was easy to parallelize and could operate on real data set. Experiments showed that this algorithm could improve recommendation performance.

Key words: time factor, matrix factorization, mean reciprocal rank, recommendation system, collaborative filtering

CLC Number: 

  • TP181
[1] LYU L Y, MEDO M, YEUNG C H, et al. Recommender systems[J]. The Journal of Physics Reports, 2012, 519(1):1-50.
[2] RICCI F, ROKACH L, SHAPIRA B, et al. Recommender systems handbook[M]. Berlin, Germany: Springer-Verlag, 2011.
[3] LEE D, SEUNG H. Algorithms for non-negative matrix factorization[J]. Advances in Neural Information Processing System, 2001, 32(6):556-562.
[4] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[J]. Advances in Neural Information Processing Systems, 2012:1257-1264.
[5] SALAKHUTDINOV R, MNIH A. Bayesian probabilistic matrix factorization using markov chain monte carlo[C] // Proceedings of the 25th International Conference on Machine Learning. New York, USA: ACM, 2008:880-887.
[6] LEE J, KIM S, LEBANON G, et al. Local low-rank matrix approximation[J]. Journal of Machine Learning Research, 2013, 28(2):82-90.
[7] KOREN Y. Factor in the neighbors: Scalable and accurate collaborative filtering[J]. ACM Transactions on Knowledge Discovery from Data, 2010, 4(1):1-24.
[8] CREMONESI P, KOREN Y, TURRIN R. Performance of recommender algorithms on top-n recommendation tasks[C] //Proceedings of the fourth ACM Conference on Recommender Systems. New York, USA: ACM, 2013:39-46.
[9] DING Y, LI X. Time weight collaborative filtering[C] //Proceedings of the 14th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2005:485-492.
[10] GONG S J, CHENG G H. Mining user interest change for improving collaborative filtering[C] //Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application. Washington DC, USA: IEEE Computer Society, 2008:24-27.
[11] LEE T Q, PARK Y, PARK Y T. A time-based approach to effective recommender systems using implicit feedback[J]. Expert Systems with Applications, 2008, 34(4): 3055-3062.
[12] BURGES C, SHAKED T, RENSHAW E, et al. Learning to rank using gradient descent[C] //Proceedings of the 22nd International Conference on Machine Learning. New York, USA: ACM, 2005:89-96.
[13] RENDLE S, FREUDENTHALER C. Improving pairwise learning for item recommendation from implicit feedback[C] //Proceedings of the 7th ACM International Conference on Web Search and Cata Mining. New York, USA: ACM, 2014:273-282.
[14] CAO Z, QIN T, LIU T Y, et al. Learning to rank: from pairwise approach to listwise approach[C] //Proceedings of the 24th International Conference on Machine Learning. New York, USA: ACM, 2007:129-136.
[15] XU J, LIU T Y, LU M, et al. Directly optimizing evaluation measures in learning to rank[C] //Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2008: 107-114.
[16] WEIMER M, KARATZOGLOU A, LE Q V, et al. CofiRank-maximum margin matrix factorization for collaborative ranking[C] //Neural Information Processing Systems. Vancouver, Canada: ACM, 2007:3-8.
[17] SHI Y, KARATZOGLOU A, BALTRUNAS L, et al. TFMAP: optimizing map for top-n context-aware recommendation[C] //Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2012:155-164.
[18] SHI Y, KARATZOGLOU A, BALTRUNAS L, et al. CLIMF: learning to maximize reciprocal rank with collaborative less-is-more filtering[C] //Proceedings of the Sixth ACM Conference on Recommender Systems. New York, USA: ACM, 2012:139-146.
[19] KABBUR S, XIA N, KARYPIS G. FISM: factored item similarity models for top-N recommender systems[C] //Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2010: 659-667.
[20] GROUPLENS. Datasets Instruction[EB/OL]. [2015-04-15]. http://grouplens.org/datasets/movielens.
[21] LEE J, SUN M, LEBANON G. PREA [EB/OL].(2013-06-13)[2015-04-10]. http://prea.gatech.edu/download.html#ver20.
[22] LEE J, SUN M, LEBANON G. PREA: personalized recommendation algorithms toolkit[J]. The Journal of Machine Learning Research, 2012, 13(1):2699-2703.
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