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Recommendations Based on Listwise Learning-to-Rank by Incorporating Social Information

  • Fang, Chen (Zhengzhou Information Science and Technology Institute) ;
  • Zhang, Hengwei (Zhengzhou Information Science and Technology Institute) ;
  • Zhang, Ming (Zhengzhou Information Science and Technology Institute) ;
  • Wang, Jindong (Zhengzhou Information Science and Technology Institute)
  • Received : 2017.04.13
  • Accepted : 2017.09.17
  • Published : 2018.01.31

Abstract

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

Keywords

References

  1. Zhao Z, Yang Q, Cai D, et al, "Expert finding for community-based question answering via ranking metric network learning," in Proc. of International Joint Conference on Artificial Intelligence, pp. 3000-3006, July 9-15, 2016.
  2. Liu J, Tang M, Zheng Z, et al, "Location-aware and personalized collaborative filtering for web service recommendation," International Journal of Computer Engineering In Research Trends, vol. 3, no. 5, pp. 356-360, 2016.
  3. Hu Y, Peng Q, Hu X, et al, "Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering," IEEE Transactions on Services Computing, vol. 8, no. 5, pp. 782-794, 2015. https://doi.org/10.1109/TSC.2014.2381611
  4. Liu N N, Yang Q, "EigenRank: a ranking-oriented approach to collaborative filtering," in Proc. of International ACM SIGIR Conference on Research and Development in Information Retrieval ACM, pp. 83-90, July 20-24, 2008.
  5. Grotov A, Rijke M D, "Online learning to rank for information retrieval: SIGIR 2016 Tutorial," in Proc. of International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1215-1218, July 17-21, 2016.
  6. Zhao Z, Zhang L, He X, et al, "Expert finding for question answering via graph regularized matrix completion," IEEE Transactions on Knowledge & Data Engineering, vol.27, no.4, pp. 993-1004, 2015. https://doi.org/10.1109/TKDE.2014.2356461
  7. Lei Y, Wang Z, Meng L, et al, "Clustering and recommendation for semantic web service in time series," KSII Transactions on Internet And Information Systems, vol.8, no.8, pp. 2743-2762, 2014. https://doi.org/10.3837/tiis.2014.08.010
  8. Kang G, Tang M, Liu J, et al, "Diversifying web service recommendation results via exploring service usage history," IEEE Transactions on Services Computing, vol. 9, no. 4, pp. 566-579, 2016. https://doi.org/10.1109/TSC.2015.2415807
  9. Ricci F, Rokach L, Shapira B, "Introduction to recommender systems handbook," Recommender Systems Handbooks, vol.22, no.1, pp. 1-4, 2011.
  10. Wang Y B, Meng X W, Hu X, "Information aging-based collaborative filtering recommendation algorithm," Journal of Electronics & Information Technology, vol. 35, no. 10, pp. 2391-2396, 2013. https://doi.org/10.3724/SP.J.1146.2012.01743
  11. Jia D, Zhang F, "A collaborative filtering recommendation algorithm based on double neighbor choosing strategy," Journal of Computer Research & Development, vol.50, no.5, pp. 1076-1084, 2013.
  12. Cong L, Liang C, Li M, "A collaborative filtering recommendation algorithm based on domain nearest neighbor," Journal of Computer Research & Development, vol.45, no.9, pp. 1532-1538, 2008.
  13. Guo H Y, Liu G S, Su B, et al, "Collaborative filtering recommendation algorithm combining community structure and interest clusters," Journal of Computer Research and Development, vol.53, no.8, pp. 1664-1672, 2016.
  14. Jamali M, Ester M, "A matrix factorization technique with trust propagation for recommendation in social networks," in Proc. of ACM Conference on Recommender Systems, pp. 135-142, September 26-30, 2010.
  15. Yang X, Steck H, Liu Y, "Circle-based recommendation in online social networks," in Proc. of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1267-1275, August 12-16, 2012.
  16. Pan W, Zhong H, Xu C, et al, "Adaptive bayesian personalized ranking for heterogeneous implicit feedbacks," Knowledge-Based Systems, vol.73, no.1, pp. 173-180, 2015.
  17. Shi Y, Larson M, Hanjalic A, "List-wise learning to rank with matrix factorization for collaborative filtering," in Proc. of ACM Conference on Recommender Systems, pp. 269-272, September 26-30, 2010.
  18. Weimer M, Karatzoglou A, Le Q V, et al, "COFI RANK, maximum margin matrix factorization for collaborative ranking," in Proc. of International Conference on Neural Information Processing Systems, pp. 1593-1600, December 03-06, 2007.
  19. Huang S, Wang S, Liu T Y, et al, "Listwise collaborative filtering," in Proc. of International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 343-352, August 09-13, 2015.
  20. Mollica C, Tardella L, "Bayesian mixture of Plackett-Luce models for partially ranked data," Statistics, 2015.
  21. Cao Z, Qin T, Liu T Y, et al, "Learning to rank: from pairwise approach to listwise approach," in Proc. of International Conference on Machine Learning, pp. 129-136, June 20-24, 2007.
  22. Morita C, Tsukimoto H, "Knowledge discovery from numerical data," Knowledge-Based Systems, vol.10, no.7, pp. 413-419, 1998. https://doi.org/10.1016/S0950-7051(98)00040-9
  23. Zheng X, Luo Y, Xu Z, et al, "Tourism destination recommender system for the cold start problem," KSII Transactions on Internet And Information Systems, vol.10, no.7, pp. 3192-3212, 2016. https://doi.org/10.3837/tiis.2016.07.018
  24. Ma H, Yang H, Lyu M R, et al, "SoRec: social recommendation using probabilistic matrix factorization," in Proc. of ACM Conference on Information & Knowledge Management, pp. 931-940, October 26-30, 2008.
  25. Barinova O, Lempitsky V, Kholi P, "On detection of multiple object instances using hough transforms," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. 9, pp. 1773-1784, 2012. https://doi.org/10.1109/TPAMI.2012.79
  26. Oren M, Papageorgiou C, Sinha P, et al, "Pedestrian detection using wavelet templates," in Proc. of 1997 Conference on Computer Vision and Pattern Recognition, pp. 193, June 17-19, 1997.
  27. Kalervo Järvelin and et al, "IR evaluation methods for retrieving highly relevant documents," in Proc. of International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 41-48, July 24-28, 2000.
  28. Chapelle O, Metlzer D, Zhang Y, et al, "Expected reciprocal rank for graded relevance," in Proc. of ACM Conference on Information and Knowledge Management, pp. 621-630, November 02-06, 2009.
  29. Salakhutdinov R, Mnih A, "Bayesian probabilistic matrix factorization using markov chain monte carlo," in Proc. of International Conference on Machine Learning, pp. 880-887, July 05-09, 2008.
  30. Liu J, Wu C, Xiong Y, et al, "List-wise probabilistic matrix factorization for recommendation," Information Sciences, vol. 278, pp. 434-447, 2014.