微信服务号

微信订阅号

2025年3月25日 22:54 星期二
首页 > 过刊浏览>2016年第27卷第3期 >691-713. DOI:10.13328/j.cnki.jos.004948
PDF HTML阅读 XML下载 导出引用 引用提醒
基于排序学习的推荐算法研究综述
作者:
基金项目:

国家自然科学基金(61272268,61103069);国家重点基础研究发展计划(973)(2014CB340404);教育部新世纪优秀人才支持计划(NCET-12-0413);霍英东教育基金会高等院校青年教师基金(142002);上海市青年科技启明星计划(15QA1403900)


Survey on Learning-to-Rank Based Recommendation Algorithms
Author:
Fund Project:

National Natural Science Foundation of China (61272268, 61103069); National Grand Fundamental Research Program of China (973) (2014CB340404); Program for New Century Excellent Talents in University (NCET-12-0413); Fok Ying Tung Education Foundation (142002); Shanghai Rising-Star Program (15QA1403900)

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [150]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    排序学习技术尝试用机器学习的方法解决排序问题,已被深入研究并广泛应用于不同的领域,如信息检索、文本挖掘、个性化推荐、生物医学等.将排序学习融入推荐算法中,研究如何整合大量用户和物品的特征,构建更加贴合用户偏好需求的用户模型,以提高推荐算法的性能和用户满意度,成为基于排序学习推荐算法的主要任务.对近些年基于排序学习的推荐算法研究进展进行综述,并对其问题定义、关键技术、效用评价、应用进展等进行概括、比较和分析.最后,对基于排序学习的推荐算法的未来发展趋势进行探讨和展望.

    Abstract:

    Learning to rank(L2R) techniques try to solve sorting problems using machine learning methods, and have been well studied and widely used in various fields such as information retrieval, text mining, personalized recommendation, and biomedicine.The main task of L2R based recommendation algorithms is integrating L2R techniques into recommendation algorithms, and studying how to organize a large number of users and features of items, build more suitable user models according to user preferences requirements, and improve the performance and user satisfaction of recommendation algorithms.This paper surveys L2R based recommendation algorithms in recent years, summarizes the problem definition, compares key technologies and analyzes evaluation metrics and their applications.In addition, the paper discusses the future development trend of L2R based recommendation algorithms.

    参考文献
    [1] George G, Haas MR, Pentland A. Big data and management. Academy of Management Journal, 2014,57(2):321-326.[doi:10. 5465/amj.2014.4002]
    [2] Gantz J, Reinsel D. The Digital Universe in 2020:Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. IDC iView:IDC Analyze the Future, 2012. 1-16.
    [3] Xu H, Zhang R, Lin C, Gan W. Construction of E-commerce recommendation system based on semantic annotation of ontology and user preference. TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014,12(3):2028-2035.[doi:10.11591/telkomnika.v12i3.4132]
    [4] Gupta Y, Saini A, Saxena AK. A new fuzzy logic based ranking function for efficient information retrieval system. Expert Systems with Applications, 2015,42(3):1223-1234.[doi:10.1016/j.eswa.2014.09.009]
    [5] Colombo-Mendoza LO, Valencia-García R, Rodríguez-González A, Samper-Zapaterd JJ. RecomMetz:A context-aware knowledgebased mobile recommender system for movie showtimes. Expert Systems with Applications, 2015,42(3):1202-1222.[doi:10. 1016/j.eswa.2014.09.016]
    [6] Gavalas D, Kenteris M. A Web-based pervasive recommendation system for mobile tourist guides. Personal and Ubiquitous Computing, 2011,15(7):759-770.[doi:10.1007/s00779-011-0389-x]
    [7] Popescul A, Pennock DM, Lawrence S. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In:Proc. of the 7th Conf. on Uncertainty in artificial intelligence. San Francisco:Morgan Kaufmann Publishers, 2001. 437-444.
    [8] Arora G, Kumar A, Devre GS, Ghumare A. Movie recommendation system based on users' similarity. Int'l Journal of Computer Science and Mobile Computing, 2014,3(4):765-770.
    [9] Ekstrand MD, Riedl JT, Konstan JA. Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction, 2011,4(2):81-173.[doi:10.1561/1100000009]
    [10] Linden G, Smith B, York J. Amazon.com recommendations:Item-to-Item collaborative filtering. IEEE Internet Computing, 2003, 7(1):76-80.[doi:10.1109/MIC.2003.1167344]
    [11] Cai Y, Leung H, Li Q, Min H, Tang J, Li H. Typicality-Based collaborative filtering recommendation. IEEE Trans. on Knowledge and Data Engineering, 2014,26(3):766-779.[doi:10.1109/TKDE.2013.7]
    [12] Burke R. Hybrid recommender systems:Survey and experiments. User Modeling and User-Adapted Interaction, 2002,12(4):331-370.[doi:10.1023/A:1021240730564]
    [13] Kagita VR, Pujari AK, Padmanabhan V. Virtual user approach for group recommender systems using precedence relations. Information Sciences, 2015,294:15-30.[doi:10.1016/j.ins.2014.08.072]
    [14] Chen W, Niu Z, Zhao X, Li Y. A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, 2014, 17(2):271-284.[doi:10.1007/s11280-012-0187-z]
    [15] Pessiot JF, Truong V, Usunier N, Amini M, Gallinari P. Learning to rank for collaborative filtering. In:Proc. of the Int'l Conf. on Enterprise Information Systems. New York:ACM Press, 2007. 145-151.
    [16] Karatzoglou A, Baltrunas L, Shi Y. Learning to rank for recommender systems. In:Proc. of the 7th ACM Conf. on Recommender Systems. ACM Press, 2013. 493-494.[doi:10.1145/2507157.2508063]
    [17] Li H. Learning to Rank for Information Retrieval and Natural Language Processing. 2nd ed., Toronto:Synthesis Lectures on Human Language Technologies, 2014. 1-121.[doi:10.2200/S00348ED1V01Y201104HLT012]
    [18] Cao Z, Qin T, Liu TY. Learning to rank:From pairwise approach to listwise approach. In:Proc. of the 24th Int'l Conf. on Machine Learning. New York:ACM Press, 2007. 129-136.[doi:10.1145/1273496.1273513]
    [19] Hang LI. A short introduction to learning to rank. IEICE Trans. on Information and Systems, 2011,94(10):1854-1862.[doi:10.1587/transinf.E94.D.1854]
    [20] Xu J, Liu TY, Lu M. Directly optimizing evaluation measures in learning to rank. In:Proc. of the 31st Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. New York:ACM Press, 2008. 107-114.[doi:10.1145/1390334. 1390355]
    [21] Kang C, Yin D, Zhang R, Torzec N, He J, Chang Y. Learning to rank related entities in Web search. Neurocomputing, 2015,166:309-318.[doi:10.1016/j.neucom.2015.04.004]
    [22] Weimer M, Karatzoglou A, Le QV, Smola A. Cofirank maximum margin matrix factorization for collaborative ranking. In:Proc. of the 21th Int'l Conf. on Neural Information Processing Systems. Berlin, Heidelberg:Springer-Verlag, 2007. 1-8.
    [23] Sharma A, Yan B. Pairwise learning in recommendation:Experiments with community recommendation on linkedin. In:Proc. of the 7th ACM Conf. on Recommender Systems. New York:ACM Press, 2013. 193-200.[doi:10.1145/2507157.2507175]
    [24] Shi Y, Larson M, Hanjalic A. List-Wise learning to rank with matrix factorization for collaborative filtering. In:Proc. of the 4th ACM Conf. on Recommender Systems. New York:ACM Press, 2010. 269-272.[doi:10.1145/1864708.1864764]
    [25] Shi Y, Karatzoglou A, Baltrunas L, Larson M. Climf:Learning to maximize reciprocal rank with collaborative less-is-more filtering. In:Proc. of the 6th ACM Conf. on Recommender Systems. New York:ACM Press, 2012. 139-146.[doi:10.1145/2365952.2365981]
    [26] Shi Y, Karatzoglou A, Baltrunas L, Larson M, Hanjalic A. xCLiMF:Optimizing expected reciprocal rank for data with multiple levels of relevance. In:Proc. of the7th ACM Conf. on Recommender Systems. New York:ACM Press, 2013. 431-434.[doi:10. 1145/2507157.2507227]
    [27] Canuto SD, Belém FM, Almeida JM, Marcos A. A comparative study of learning-to-rank techniques for tag recommendation. Journal of Information and Data Management, 2013,4(3):453-462.
    [28] Lv Y, Moon T, Kolari P, Zheng ZH, Wang XH, Chang Y. Learning to model relatedness for news recommendation. In:Proc. of the 20th Int'l Conf. on World Wide Web. New York:ACM Press, 2011. 57-66.[doi:10.1145/1963405.1963417]
    [29] Yao W, He J, Huang G, Zhang Y. SoRank:Incorporating social information into learning to rank models for recommendation. In:Proc. of the Companion Publication of the 23rd Int'l Conf. on World Wide Web Companion. New York:ACM Press, 2014. 409-410.[doi:10.1145/2567948.2577333]
    [30] Yao W, He J, Wang H, Zhang YC, Cao J. Collaborative topic ranking:Leveraging item meta-data for sparsity reduction. In:Proc. of the 29th AAAI Conf. on Artificial Intelligence. 2015. 374-380.
    [31] Zhou SL, Xu JG. Learning to rank algorithm for microblogs based on analysis features and dynamic stepsize. Ruan Jian Xue Bao/Journal of Software, 2013,24:150-161(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/13033.htm
    [32] Hong L, Doumith AS, Davison BD. Co-Factorization machines:Modeling user interests and predicting individual decisions in Twitter. In:Proc. of the 6th ACM Int'l Conf. on Web Search and Data Mining. New York:ACM Press, 2013. 557-566.[doi:10. 1145/2433396.2433467]
    [33] Liu NN, Yang Q. Eigenrank:A ranking-oriented approach to collaborative filtering. In:Proc. of the 31st Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM Press, 2008. 83-90.[doi:10.1145/1390334.1390351]
    [34] Zhao XW, Guo Y, He Y, Jiang H, Wu YX, Li XM. We know what you want to buy:A demographic-based system for product recommendation on microblogs. In:Proc. of the 20th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York:ACM Press, 2014. 1935-1944.[doi:10.1145/2623330.2623351]
    [35] Wu H, Hu Y, Li H, Chen EH. A new approach to query segmentation for relevance ranking in Web search. Information Retrieval Journal, 2015,18(1):26-50.[doi:10.1007/s10791-014-9246-7]
    [36] Chen T, Tang L, Liu Q, Yang DY, Xie SN, Cao XZ, Wu CY, Yao EP, Liu ZY, Jiang ZS, Chen C, Kong WH, Yu Y. Combining factorization model and additive forest for collaborative followee recommendation. In:Proc. of the KDD-Cup Workshop. 2012.
    [37] Yu J, Tao D, Wang M, Rui Y. Learning to rank using user clicks and visual features for image retrieval. IEEE Trans. on Cybernetics, 2015,45(4):767-779.[doi:10.1109/TCYB.2014.2336697]
    [38] Ding YX, Yan ZQ, Feng W, Xu CL, Zhou D. Rank learning based on supervised topic model. Acta Electronica Sinica, 2015,43(2):333-337(in Chinese with English abstract).[doi:10.3969/j.issn.0372-2112.2015.02.019]
    [39] Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. In:Proc. of the Advances in artificial intelligence 2009. 2009. 4.[doi:10.1155/2009/421425]
    [40] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering, 2005,17(6):734-749.[doi:10.1109/TKDE.2005.99]
    [41] Pazzani MJ, Billsus D. Content-Based recommendation systems. In:Proc. of the Adaptive Web. Berlin, Heidelberg:Springer- Verlag, 2007. 325-341.[doi:10.1007/978-3-540-72079-9_10]
    [42] Baltrunas L, Ricci F. Experimental evaluation of context-dependent collaborative filtering using item splitting. User Modeling and User-Adapted Interaction, 2014,24(1-2):7-34.[doi:10.1007/s11257-012-9137-9]
    [43] Klašnja-Milićević A, Vesin B, Ivanović M, Budimac Z. E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 2011,56(3):885-899.[doi:10.1016/j.compedu.2010.11.001]
    [44] Wen H, Fang L, Guan L. A hybrid approach for personalized recommendation of news on the Web. Expert Systems with Applications, 2012,39(5):5806-5814.[doi:10.1016/j.eswa.2011.11.087]
    [45] Nguyen TTS, Lu HY, Lu J. Web-Page recommendation based on Web usage and domain knowledge. IEEE Trans. on Knowledge and Data Engineering, 2014,26(10):2574-2587.[doi:10.1109/TKDE.2013.78]
    [46] Blanco-Fernández Y, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, Lopez-Nores M, Garcia-Duque J, Fernandez-Vilas A, Diaz- Redondo RP, Bermejo-Munoz J. A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowledge-Based Systems, 2008,21(4):305-320.[doi:10.1016/j.knosys.2007.07.004]
    [47] Movahedian H, Khayyambashi MR. A semantic recommender system based on frequent tag pattern. Intelligent Data Analysis, 2015, 19(1):109-126.[doi:10.3233/IDA-140699]
    [48] Salton G, Fox EA, Wu H. Extended Boolean information retrieval. Communications of the ACM, 1983,26(11):1022-1036.[doi:10.1145/182.358466]
    [49] Berry MW, Drmac Z, Jessup ER. Matrices, vector spaces, and information retrieval. SIAM Review, 1999,41(2):335-362.[doi:10.1137/S0036144598347035]
    [50] Blei DM. Probabilistic topic models. Communications of the ACM, 2012,55(4):77-84.[doi:10.1145/2133806.2133826]
    [51] Zhai C, Lafferty J. A study of smoothing methods for language models applied to information retrieval. ACM Trans. on Information Systems (TOIS), 2004,22(2):179-214.[doi:10.1145/984321.984322]
    [52] Campos R, Dias G, Jorge AM, Jatowt A. Survey of temporal information retrieval and related applications. ACM Computing Surveys (CSUR), 2014,47(2):15.[doi:10.1145/2619088]
    [53] Shaw B, Shea J, Sinha S, Hogue A. Learning to rank for spatiotemporal search. In:Proc. of the 6th ACM Int'l Conf. on Web Search and Data Mining. New York:ACM Press, 2013. 717-726.[doi:10.1145/2433396.2433485]
    [54] Fuhr N. Optimum polynomial retrieval functions based on the probability ranking principle. ACM Trans. on Information Systems (TOIS), 1989,7(3):183-204.[doi:10.1145/65943.65944]
    [55] Cao Y, Xu J, Liu TY, Li H, Huang YL, Hon HW. Adapting ranking SVM to document retrieval. In:Proc. of the 29th Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. New York:ACM Press, 2006. 186-193.[doi:10.1145/1148170.1148205]
    [56] Cao H, Verma R, Nenkova A. Speaker-Sensitive emotion recognition via ranking:Studies on acted and spontaneous speech. Computer Speech & Language, 2015,29(1):186-202.[doi:10.1016/j.csl.2014.01.003]
    [57] Song Y, Wang H, He X. Adapting deep ranknet for personalized search. In:Proc. of the 7th ACM Int'l Conf. on Web Search and Data Mining. New York:ACM Press, 2014. 83-92.[doi:10.1145/2556195.2556234]
    [58] Freund Y, Iyer R, Schapire RE, Singer Y. An efficient boosting algorithm for combining preferences. The Journal of Machine Learning Research, 2003,4:933-969.
    [59] Miao Z, Wang J, Zhou A, Tang K. Regularized boost for semi-supervised ranking. In:Proc. of the 18th Asia Pacific Symp. on Intelligent and Evolutionary Systems. Berlin, Heidelberg:Springer-Verlag, 2015. 643-651.[doi:10.1007/978-3-319-13359-1_49]
    [60] Liu TY. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 2009,3(3):225-331.
    [61] Hüllermeier E, Fürnkranz J, Cheng W, Brinker K. Label ranking by learning pairwise preferences. Artificial Intelligence, 2008, 172(16):1897-1916.[doi:10.1016/j.artint.2008.08.002]
    [62] Hofmann K, Whiteson S, de Rijke M. Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval. Information Retrieval, 2013,16(1):63-90.[doi:10.1007/s10791-012-9197-9]
    [63] Xia F, Liu TY, Wang J, Li H. Listwise approach to learning to rank:Theory and algorithm. In:Proc. of the 25th Int'l Conf. on Machine Learning. New York:ACM Press, 2008. 1192-1199.[doi:10.1145/1390156.1390306]
    [64] Xie B, Tang X, Tang F. Hybrid recommendation base on learning to rank. In:Proc. of the 20159th Int'l Conf. on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, 2015. 53-57.[doi:10.1109/IMIS.2015.13]
    [65] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009,42(8):30-37.[doi:10.1109/MC.2009.263]
    [66] Sotiras A, Resnick SM, Davatzikos C. Finding imaging patterns of structural covariance via non-negative matrix factorization. NeuroImage, 2015,108:1-16.[doi:10.1016/j.neuroimage.2014.11.045]
    [67] Bach F. Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression. The Journal of Machine Learning Research, 2014,15(1):595-627.
    [68] Koren Y, Sill J. OrdRec:An ordinal model for predicting personalized item rating distributions. In:Proc. of the 5th ACM Conf. on Recommender Systems. New York:ACM Press, 2011. 117-124.
    [69] Yin J, Wang ZS, Li Q, Su WJ. Personalized recommendation based on large-scale implicit feedback. Ruan Jian Xue Bao/Journal of Software, 2014,25(9):1953-1966(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4648.htm[doi:10.13328/j. cnki.jos.004648]
    [70] Sun JK. Research and implementation of ranking based personalized recommender algorithms[M.S. Thesis]. Ji'nan:Shandong University, 2014(in Chinese with English abstract).
    [71] Croux C, Dehon C. Influence functions of the Spearman and Kendall correlation measures. Statistical Methods & Applications, 2010, 19(4):497-515.[doi:10.1007/s10260-010-0142-z]
    [72] Puth MT, Neuhäuser M, Ruxton GD. Effective use of Spearman's and Kendall's correlation coefficients for association between two measured traits. Animal Behaviour, 2015,102:77-84.[doi:10.1016/j.anbehav.2015.01.010]
    [73] 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. Oregon:AUAI, 2009. 452-461.
    [74] Pan W, Chen L. Cofiset:Collaborative filtering via learning pairwise preferences over item-sets. In:Proc. of the SIAM Int'l Conf. on Data Mining. Philadelphia:SAIM, 2013. 180-188.
    [75] Takács G, Tikk D. Alternating least squares for personalized ranking. In:Proc. of the 6th ACM Conf. on Recommender Systems. New York:ACM Press, 2012. 83-90.[doi:10.1145/2365952.2365972]
    [76] Yang SH, Long B, Smola AJ, Zha HY, Zheng ZH. Collaborative competitive filtering:learning recommender using context of user choice. In:Proc. of the 34th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. New York:ACM Press, 2011. 295-304.[doi:10.1145/2009916.2009959]
    [77] Liu J, Wu C, Xiong Y, Liu WY. List-Wise probabilistic matrix factorization for recommendation. Information Sciences, 2014,278:434-447.[doi:10.1016/j.ins.2014.03.063]
    [78] Ceberio J, Mendiburu A, Lozano J. The Plackett-Luce ranking model on permutation-based optimization problems. In:Proc. of the 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. 494-501.[doi:10.1109/CEC.2013.6557609]
    [79] Le QV, Smola AJ. Direct optimization of ranking measures. Technical Report, NICTA, Canberra, Australia, 2007.
    [80] Ronkin LI. Jessen theorems for holomorphic, almost-periodic functions in tubular domains. Siberian Mathematical Journal, 1987, 28(3):510-514.[doi:10.1007/BF00969587]
    [81] 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. New York:ACM Press, 2012. 155-164.[doi:10.1145/2348283.2348308]
    [82] Johnson R, Zhang T. Learning nonlinear functions using regularized greedy forest. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2014,36(5):942-954.[doi:10.1109/TPAMI.2013.159]
    [83] Donmez P, Svore KM, Burges CJC. On the local optimality of LambdaRank. In:Proc. of the 32nd Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. New York:ACM Press, 2009. 460-467.[doi:10.1145/1571941.1572021]
    [84] Xiang L. Recommender System Practice. Beijing:Posts and Telecom Press, 2012. 40-55(in Chinese).
    [85] Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G. Learning to rank using gradient descent. In:Proc. of the 22nd Int'l Conf. on Machine Learning. ACM Press, 2005. 89-96.[doi:10.1145/1102351.1102363]
    [86] Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Trans. on Information Systems (TOIS), 2002, 20(4):422-446.[doi:10.1145/582415.582418]
    [87] Meng XW, Liu SD, Zhang YJ, Hu X. Research on social recommender systems. Ruan Jian Xue Bao/Journal of Software, 2015,26(6):1356-1372(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4831.htm[doi:10.13328/j.cnki.jos. 004831]
    [88] Duan Y, Jiang L, Qin T, Zhou M, Shum HY. An empirical study on learning to rank of tweets. In:Proc. of the 23rd Int'l Conf. on Computational Linguistics. Berlin, Heidelberg:Springer-Verlag, 2010. 295-303.
    [89] Peng ZH, Sun L, Han XP, Shi B. Micro-blog user recommendation using learning to rank. Journal of Chinese Information Processing, 2013,27(4):96-102(in Chinese with English abstract).[doi:10.3969/j.issn.1003-0077.2013.04.015]
    [90] Elmongui HG, Mansour R, Morsy H, Khater S, El-Sharkasy A, Ibrahim R. TRUPI:Twitter recommendation based on users' personal Interests. In:Proc. of the Computational Linguistics and Intelligent Text Processing. Springer Int'l Publishing, 2015. 272-284.[doi:10.1007/978-3-319-18117-2_20]
    [91] Liu JB. Microblog recommender system based on collaborative filtering and behavior analysis[MS. Thesis]. Nanjing:Nanjing University of Science and Technology, 2014(in Chinese with English abstract).
    [92] McFee B, Barrington L, Lanckriet G. Learning content similarity for music recommendation. IEEE Trans. on Audio, Speech, and Language Processing, 2012,20(8):2207-2218.[doi:10.1109/TASL.2012.2199109]
    [93] Zhao X, Li G, Wang M, Yuan J, Zha ZJ, Li ZJ, Chua TS. Integrating rich information for video recommendation with multi-task rank aggregation. In:Proc. of the 19th ACM Int'l Conf. on Multimedia. New York:ACM Press, 2011. 1521-1524.[doi:10. 1145/2072298.2072055]
    [94] Wu L, Yang L, Yu N, Hua XS. Learning to tag. In:Proc. of the 18th Int'l Conf. on World Wide Web. New York:ACM Press, 2009. 361-370.[doi:10.1145/1526709.1526758]
    [95] Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recommendation. In:Proc. of the 3rd ACM Int'l Conf. on Web Search and Data Mining. New York:ACM Press, 2010. 81-90.[doi:10.1145/1718487.1718498]
    [96] Zhou YM. Label ranking methods based on Gaussian mixture model[MS. Thesis]. Hangzhou:Zhejiang University, 2014(in Chinese).
    [97] Li C, Feng S, Shang W. Research on personalized recommendation algorithm for internet user to browse news. In:Proc. of the 2015 Int'l Conf. on Automation, Mechanical Control and Computational Engineering. Atlantis Press, 2015.
    [98] Lu Z, Dou Z, Lian J, Xie X, Yang Q. Content-Based collaborative filtering for news topic recommendation. In:Proc. of the 29th AAAI Conf. on Artificial Intelligence. 2015.
    [99] De Francisci Morales G, Gionis A, Lucchese C. From chatter to headlines:harnessing the real-time Web for personalized news recommendation. In:Proc. of the 5th ACM Int'l Conf. on Web Search and Data Mining. New York:ACM Press, 2012. 153-162.[doi:10.1145/2124295.2124315]
    [100] Das M, De Francisci Morales G, Gionis A, Weber I. Learning to question:Leveraging user preferences for shopping advice. In:Proc. of the 19th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York:ACM Press, 2013. 203-211.[doi:10.1145/2487575.2487653]
    [101] Liu S, Feng J, Song Z, Zhang TZ. Hi, magic closet, tell me what to wear! In:Proc. of the 20th ACM Int'l Conf. on Multimedia. New York:ACM Press, 2012. 619-628.[doi:10.1145/2393347.2393433]
    [102] Meng XW, Hu X, Wang LC, Zhang YJ. Mobile recommender systems and their applications. Ruan Jian Xue Bao/Journal of Software, 2013,24(1):91-108(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4292.htm[doi:10.3724/SP.J. 1001.2013.04292]
    [103] Zheng VW, Zheng Y, Xie X, Qiang Y. Towards mobile intelligence:Learning from GPS history data for collaborative recommendation. Artificial Intelligence, 2012,184:17-37.[doi:10.1016/j.artint.2012.02.002]
    [104] Glover F, Ye T, Punnen AP, Kochenberger G. Integrating tabu search and VLSN search to develop enhanced algorithms:A case study using bipartite Boolean quadratic programs. European Journal of Operational Research, 2015,241(3):697-707.[doi:10.1016/j.ejor.2014.09.036]
    [105] Zhuang J, Mei T, Hoi SCH, Xy YQ, Li SP. When recommendation meets mobile:Contextual and personalized recommendation on the go. In:Proc. of the 13th Int'l Conf. on Ubiquitous Computing. New York:ACM Press, 2011. 153-162.[doi:10.1145/2030112. 2030134]
    [106] Jiang K. Geo-Referenced social media mining and its application[Ph.D. Thesis]. Hefei:University of Science and Technology of China, 2014(in Chinese).
    [107] Lian DF. Data mining on location-based social networks[Ph.D. Thesis]. Hefei:University of Science and Technology of China, 2014(in Chinese).
    [108] Wang L, Lin J, Metzler D. Learning to efficiently rank. In:Proc. of the 33rd Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. New York:ACM Press, 2010. 138-145.[doi:10.1145/1835449.1835475]
    [109] Han P, Xie B, Yang F, Shen R. A scalable P2P recommender system based on distributed collaborative filtering. Expert Systems with Applications, 2004,27(2):203-210.[doi:10.1016/j.eswa.2004.01.003]
    [110] Abbas A, Bilal K, Zhang L, Khan SU. A cloud based health insurance plan recommendation system:A user centered approach. Future Generation Computer Systems, 2015,43:99-109.
    [111] Ueda N, Nakano R. Generalization error of ensemble estimators. In:Proc. of IEEE Int'l Conf. on Neural Networks. New York:IEEE, 1996. 90-95.[doi:10.1109/ICNN.1996.548872]
    [112] Zhang T, Iyengar VS. Recommender systems using linear classifiers. The Journal of Machine Learning Research, 2002,2:313-334.[doi:10.1162/153244302760200641]
    [113] Li L, Zheng L, Yang F, Li T. Modeling and broadening temporal user interest in personalized news recommendation. Expert Systems with Applications, 2014,41(7):3168-3177.[doi:10.1016/j.eswa.2013.11.020]
    [114] Pfeiffer J, Scholz M. A low-effort recommendation system with high accuracy. Business & Information Systems Engineering, 2013, 5(6):397-408.[doi:10.1007/s12599-013-0295-z]
    [115] Cena F, Console L, Gena C, Goy A, Levi G, Modeo S, Torre I. Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Communications, 2006,19(4):369-384.
    [116] Zhang ML. Research on multi-task learning. Sciencepaper Online, 2011(in Chinese).
    [117] Caruana R. Multitask learning. Machine Learning, 1997,28(1):41-75.[doi:10.1023/A:1007379606734]
    [118] Phuong ND, Phuong TM. Collaborative filtering by multi-task learning. In:Proc. of the IEEE Int'l Conf. on Research, Innovation and Vision for the Future (RIVF 2008). IEEE, 2008. 227-232.[doi:10.1109/RIVF.2008.4586360]
    [119] Tan S, Bu J, Qin X, Chen C, Cai D. Cross domain recommendation based on multi-type media fusion. Neurocomputing, 2014,127:124-134.[doi:10.1016/j.neucom.2013.08.034]
    [120] Soldo F, Le A, Markopoulou A. Blacklisting recommendation system:Using spatio-temporal patterns to predict future attacks. IEEE Journal on Selected Areas in Communications, 2011,29(7):1423-1437.[doi:10.1109/JSAC.2011.110808]
    [121] Gao Y, Shen J, Chen C, Liu CY, Ye JF. A rank-based prediction algorithm of learning the intention of the query. In:Proc. of the CCIR 2009. 2009(in Chinese with English abstract).
    [122] Wang YZ, Jin XL, Cheng XQ. Network big data:Present and future. Chinese Journal of Computer 2013,36(6):1125-1138(in Chinese with English abstract).[doi:10.3724/SP.J.1016.2013.01125]
    [123] Li JZ, Yang W, Xia JW, Ceng XY, Sun LY. Learning to rank method based on Hooke & Jeeves Pattern Search. Computer Engineering, 2015,41(7):215-218(in Chinese with English abstract).[doi:10.3969/j.issn.1000-3428.2015.07.041]
    [124] Ying Y, Liu YJ, Chen C. Personalization recommender system based on cloud-computing technology. Computer Engineering and Applications, 2015,51(13):111-117(in Chinese with English abstract).[doi:10.3778/j.issn.1002-8331.1409-0134]
    [125] Huang W, Ceng SR. A novel computer-aided diagnosis method for disease severity prediction based on image information and ranking learning techniques. Journal of Nanchang Institute of Technology, 2015,34(3):33-37(in Chinese with English abstract).
    [126] Chen K, Zou Q, Peng ZP, Ke WD. Collaborative ranking friend recommendation algorithm in heterogeneous social network. Journal of Chinese Computer Systems, 2014,35(6):1270-1274(in Chinese with English abstract).
    [127] Hawalah A, Fasli M. Dynamic user profiles for web personalisation. Expert Systems with Applications, 2015,42(5):2547-2569.[doi:10.1016/j.eswa.2014.10.032]
    [128] Bobadilla J, Ortega F, Hernando A, Gutierrez A. Recommender systems survey. Knowledge-Based Systems, 2013,46:109-132.[doi:10.1016/j.knosys.2013.03.012]
    [129] Tejeda-Lorente Á, Porcel C, Peis E, Sanz R, Herrera-Viedma E. A quality based recommender system to disseminate information in a university digital library. Information Sciences, 2014,261:52-69.[doi:10.1016/j.ins.2013.10.036]
    [130] Lee S, Park S, Kahng M, Lee SG. Pathrank:Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems. Expert Systems with Applications, 2013,40(2):684-697.[doi:10.1016/j.eswa.2012.08.004]
    附中文参考文献:
    [31] 周诗龙,徐俊刚.基于分析特征与动态步长的微博排序学习算法.软件学报,2013,24:150-161. http://www.jos.org.cn/1000- 9825/13033.htm
    [38] 丁宇新,燕泽权,冯威,薛成龙,周迪.基于有监督主题模型的排序学习算法.电子学报,2015,43(2):333-337.[doi:10.3969/j.issn. 0372-2112.2015.02.019]
    [69] 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. http://www.jos.org.cn/1000- 9825/4648.htm[doi:10.13328/j.cnki.jos.004648]
    [70] 孙建凯.面向排序的个性化推荐算法研究与实现[硕士学位论文].济南:山东大学,2014.
    [84] 项亮.推荐系统实践.第1版.北京:人民邮电出版社,2012.40-55.
    [87] 孟祥武,刘树栋,张玉洁,胡勋.社会化推荐系统研究.软件学报,2015,26(6):1356-1372. http://www.jos.org.cn/1000-9825/4831.htm[doi:10.13328/j.cnki.jos.004831]
    [89] 彭泽环,孙乐,韩先培,石贝.基于排序学习的微博用户推荐.中文信息学报,2013,27(4):96-102.[doi:10.3969/j.issn.1003-0077. 2013.04.015]
    [91] 刘剑波.基于协同过滤和行为分析的微博推荐系统[硕士学位论文].南京:南京理工大学,2014.
    [96] 周扬名.基于高斯混合模型的标签排序算法研究[硕士学位论文].杭州:浙江大学,2014.
    [102] 孟祥武,胡勋,王立才,张玉洁.移动推荐系统及其应用.软件学报,2013,24(1):91-108. http://www.jos.org.cn/1000-9825/4292.htm[doi:10.3724/SP.J.1001.2013.04292]
    [106] 蒋锴.含地理位置信息的社交媒体挖掘及应用[博士学位论文].合肥:中国科学技术大学,2014.
    [107] 连德富.基于位置社交网络的数据挖掘[博士学位论文].合肥:中国科学技术大学,2014.
    [116] 张敏灵.多任务学习的研究.中国科技论文在线,2011.
    [121] 高莺,沈洁,陈沧,刘春阳,叶君峰.一种基于排序学习的查询意图预测算法.见:第5届全国信息检索学术会议论文集.2009.
    [122] 王元卓,靳小龙,程学旗.网络大数据:现状与展望.计算机学报,2013,36(6):1125-1138.[doi:10.3724/SP.J.1016.2013.01125]
    [123] 李金忠,杨威,夏洁武,曾小荟,孙凌宇.基于Hooke & Jeeves模式搜索的排序学习方法.计算机工程,2015,41(7):215-218.[doi:10. 3969/j.issn.1000-3428.2015.07.041]
    [124] 应毅,刘亚军,陈诚.基于云计算技术的个性化推荐系统.计算机工程与应用,2015,51(13):111-117.[doi:10.3778/j.issn.1002-8331. 1409-0134]
    [125] 黄伟,曾舒如.基于图像信息和排序学习技术的疾病预测方法.南昌工程学院学报,2015,34(3):33-37.
    [126] 陈珂,邹权,彭志平,柯文德.异质社交网络中协同排序的好友推荐算法.小型微型计算机系统,2014,35(6):1270-1274.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

黄震华,张佳雯,田春岐,孙圣力,向阳.基于排序学习的推荐算法研究综述.软件学报,2016,27(3):691-713

复制
分享
文章指标
  • 点击次数:9748
  • 下载次数: 20837
  • HTML阅读次数: 3585
  • 引用次数: 0
历史
  • 收稿日期:2015-02-12
  • 最后修改日期:2015-05-14
  • 录用日期:2015-12-02
  • 在线发布日期: 2015-12-30
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19731747位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号