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Home > Archive>Volume 25, Issue 9, 2014 >1953-1966. DOI:10.13328/j.cnki.jos.004648
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Personalized Recommendation Based on Large-Scale Implicit Feedback
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    Abstract:

    This paper explores the area of personalized recommendation based on large-scale implicit feedback, where only positive feedback is available. To tackle the difficulty arising from lack of negative samples, a novel latent factor model IFRM is proposed, to convert the recommendation task into adoption probability optimization problem. To further improve efficiency and scalability, a parallel version of IFRM named p-IFRM is presented. By randomly partitioning users and items into buckets and thus reconstructing update sequence, IFRM can be learnt in parallel. The study theoretically derives the model from Bayesian analysis and experimentally demonstrates its effectiveness and efficiency by implementing p-IFRM under MapReduce framework and conducting comprehensive experiments on real world large datasets. The experiment results show that the model improves recommendation quality and performs well in scalability.

    Reference
    [1] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In: Proc. of the Advances in Neural Information Processing Systems. 2007. 1257~1264.
    [2] Zhou Y, Wilkinson D, Schreiber R, Pan R. Large-Scale parallel collaborative filtering for the netflix prize. In: Proc. of the Algorithmic Aspects in Information and Management. Berlin, Heidelberg: Springer-Verlag, 2008. 337~348. [doi: 10.1007/978-3- 540-68880-8_32]
    [3] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009,42(8):30~37. [doi: 10. 1109/MC.2009.263]
    [4] Liu X, Aberer K. SoCo: A social network aided context-aware recommender system. In: Proc. of the 22nd Int’l Conf. on World Wide Web. 2013. 781~802.
    [5] Chen J, Yin J. A collaborative filtering recommendation algorithm based on influence sets. Ruan Jian Xue Bao/Journal of Software, 2007,18(7):1685~1694 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/18/1685.htm [doi: 10.1360/jos181685]
    [6] Sun GF, Wu L, Liu Q, Zhu C, Chen EH. Recommendations based on collaborative filtering by exploiting sequential behaviors. Ruan Jian Xue Bao/Journal of Software, 2013,24(11):2721~2733 (in Chinese with English abstract). http://www.jos.org.cn/1000- 9825/4478.htm [doi: 10.3724/SP.J.1001.2013.04478]
    [7] Pan R, Zhou Y, Cao B, Liu NN, Lukose R. One-Class collaborative filtering. In: Proc. of the 8th IEEE Int’l Conf. on Data Mining (ICDM 2008). IEEE, 2008. 502~511. [doi: 10.1109/ICDM.2008.16]
    [8] Jiang M, Cui P, Liu R, Yang Q, Wang F, Zhu W, Yang S. Social contextual recommendation. In: Proc. of the 21st ACM Int’l Conf. on Information and Knowledge Management. ACM Press, 2012. 45~54. [doi: 10.1145/2396761.2396771]
    [9] Chen K, Chen T, Zheng G, Jin O, Yao EP, Yu Y. Collaborative personalized tweet recommendation. In: Proc. of the 35th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM Press, 2012. 661~670. [doi: 10.1145/2348283. 2348372]
    [10] Shi Y, Karatzoglou A, Baltrunas L, Larson M, Hanjalic A, Oliver N. TFMAP: Optimizing MAP for top-n context-aware recommendation. In: Proc. of the 35th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM Press, 2012. 155~164. [doi: 10.1145/2348283.2348308]
    [11] Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: Proc. of the 8th IEEE Int’l Conf. on Data Mining (ICDM 2008). IEEE, 2008. 263~272. [doi: 10.1109/ICDM.2008.22]
    [12] Steck H. Training and testing of recommender systems on data missing not at random. In: Proc. of the 16th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. ACM Press, 2010. 713~722. [doi: 10.1145/1835804.1835895]
    [13] Herlocker JL, Konstan JA, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In: Proc. of the 22nd Annual Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM Press, 1999. 230~237. [doi: 10.1145/312624.312682]
    [14] Sarwar B, Karypis G, Konstan J, Riedl J. Item-Based collaborative filtering recommendation algorithms. In: Proc. of the 10th Int’l Conf. on World Wide Web. ACM Press, 2001. 285~295. [doi: 10.1145/371920.372071]
    [15] Kurucz M, Benczur AA, Kiss T, Nagy I, Szabo A, Torma B. Who rated what: A combination of SVD, correlation and frequent sequence mining. In: Proc. of the KDD Cup and Workshop, Vol.23. 2007. 720~727.
    [16] Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proc. of the 25th Conf. on Uncertainty in Artificial Intelligence. AUAI Press, 2009. 452~461.
    [17] Pilászy I, Zibriczky D, Tikk D. Fast als-based matrix factorization for explicit and implicit feedback datasets. In: Proc. of the 4th ACM Conf. on Recommender Systems. ACM Press, 2010. 71~78. [doi: 10.1145/1864708.1864726]
    [18] Yu HF, Hsieh CJ, Si S, Dhillon IS. Parallel matrix factorization for recommender systems. Knowledge and Information Systems, 2013. 1~27.
    [19] Niu F, Recht B, Ré C, Wright SJ. Hogwild!: A lock-free approach to parallelizing stochastic gradient descent. arXiv preprint arXiv: 1106.5730, 2011.
    [20] Gemulla R, Haas PJ, Nijkamp E, Sismanis Y. Large-Scale matrix factorization with distributed stochastic gradient descent. In: Proc. of the 17th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. ACM Press, 2011. 69~77. [doi: 10.1145/ 2020408.2020426]
    [21] Recht B, Ré C. Parallel stochastic gradient algorithms for large-scale matrix completion. Mathematical Programming Computation, 2011. 1~26.
    [22] Zhuang Y, Chin WS, Juan YC, Lin CJ. A fast parallel SGD for matrix factorization in shared memory systems. In: Proc. of the 7th ACM Conf. on Recommender Systems. ACM Press, 2013. 249~256. [doi: 10.1145/2507157.2507164]
    [23] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008,51(1): 107~113. [doi: 10.1145/1327452.1327492]
    [24] Schelter S, Boden C, Schenck M, Alexandrov A, Markl V. Distributed matrix factorization with mapreduce using a series of broadcast-joins. In: Proc. of the 7th ACM Conf. on Recommender Systems. ACM Press, 2013. 281~284. [doi: 10.1145/2507157. 2507195]
    [25] Langford J, Smola A, Zinkevich M. Slow learners are fast. arXiv preprint arXiv: 0911.0491, 2009.
    [26] Agarwal A, Duchi JC. Distributed delayed stochastic optimization. In: Proc. of the IEEE 51st Annual Conf. on Decision and Control (CDC 2012). IEEE, 2012. 5451~5452.
    [27] Apache hadoop. http://hadoop.apache.org
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印鉴,王智圣,李琪,苏伟杰.基于大规模隐式反馈的个性化推荐.软件学报,2014,25(9):1953-1966

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  • Received:April 10,2014
  • Revised:May 14,2014
  • Online: September 09,2014
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