Computer Technology, Control Technology |
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Incremental graph pattern matching based dynamic recommendation method for cold-start user |
ZHANG Ya nan, CHEN De yun, WANG Ying jie, LIU Yu peng |
1. Post-doctoral Research Station of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China;
2. Software school,Harbin University of Science and Technology, Harbin 150040,China;
3. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,China;
4. School of computer and control engineering, YanTai University, YanTai 264000,China |
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Abstract Since ignoring the change of user’s social relationships would lead to inaccurate recommendations, similar users for cold start users were updated based on social network topology incrementally, and accurate recommendations were provided based on updated similar users. User social relationships might be dynamically changed, however, the existing social network based cold-start recommendation methods did not fully consider the impact caused by the change of social relationships on recommendations as time pass by. In order to give accurate and timely in manner recommendations for cold start users, an incremental graph pattern matching based dynamic cold-start recommendation method (IGPMDCR) was proposed, which could update similar users for cold-start user incrementally, and give accurate and timely in manner recommendations for cold-start user. Experimental results on the real social network websites datasets show that IGPMDCR can give cold-start user accurately and timely in manner recommendations when user’s social relationships are changing.
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Published: 06 March 2017
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Cite this article:
ZHANG Ya nan, CHEN De yun, WANG Ying jie, LIU Yu peng. Incremental graph pattern matching based dynamic recommendation method for cold-start user. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(2): 408-415.
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基于增量图形模式匹配的动态冷启动推荐方法
针对忽视用户的社交关系变化可能得到不准确的推荐结果这一问题,为冷启动用户基于社交网络拓扑结构增量更新相似用户,并基于更新的相似用户给出准确的推荐.用户社交关系是动态变化的,然而现有基于社交网络的冷启动推荐却没有充分考虑社交关系的变更对推荐结果的影响.为了给冷启动用户实时准确的推荐,提出基于增量图形模式匹配的动态冷启动推荐方法(IGPMDCR),增量地更新冷启动用户的相似用户,为冷启动用户给出实时准确的推荐结果.在真实社交网站的数据集的实验结果表明,IGPMDCR可以在用户间社交关系变更的情况下,为冷启动用户给出实时准确的推荐结果.
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[1] BOBADILLA J S, ORTEGA F, HERNANDO A, et al. A collaborative filtering approach to mitigate the new user cold start problem[J]. Knowledge-Based Systems, 2012, 26(1): 225-238.
[2] LIKA B, KOLOMVATSOS K, HADJIEFTHYMIADES S. Facing the cold start problem in recommender systems [J]. Expert Systems with Applications, 2014, 41(4): 2065-2073.
[3] REN Y L, LI G, ZHOU W L. PRICAI 2012: Trends in Artificial Intelligence [M]. Berlin: Springer, 2012:887-890.
[4] LING Y X, GUO D K, CAI F, et al. User-based Clustering with Top-N Recommendation on Cold-Start Problem[C]∥Proceedings of the 2013 3rd International Conference on Intelligent System Design and Engineering Applications. Hong Kong: IEEE Computer Society, 2013: 1585-1589.
[5] LOPS P, DE GEMMIS M, SEMERARO G. Recommender systems handbook [M]. Berlin Heidelberg: Springer, 2011: 73105.
[6] YIN H, CUI B, CHEN L, et al. A temporal context-aware model for user behavior modeling in social media systems [C]∥Proceedings of the 2014 ACM SIGMOD international Conference on Management of Data. Snowbird, USA: ACM, 2014: 1543-1554.
[7] WANG J, DE VRIES A P, REINDERS M J T. Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C]∥Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Washington, USA: ACM, 2006: 501-508.
[8] JAMALI M, ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks [C]∥Proceedings of the 4th ACM Conference on Recommender Systems. Barcelona, Spain: ACM, 2010: 135-142.
[9] MA H, YANG H, LYU M R, et al. Sorec: social recommendation using probabilistic matrix factorization[C]∥Proceedings of the 17th ACM Conference on Information and Knowledge Management. Napa Valley, USA: ACM, 2008: 931-940.
[10] WU L, CHEN E H, LIU Q, et al. Leveraging tagging for neighborhoodaware probabilistic matrix factorization[C]∥Proceedings of the 21st ACM International Conference on Information and Knowledge Management. Maui Hawaii, USA: ACM, 2012: 1854-1858.
[11] KOREN Y. Collaborative filtering with temporal dynamics[J]. Communications of the ACM, 2010, 53(4): 89-97.
[12] REN L, GU J Z, XIA W W. An item-based collaborative filtering approach based on balanced rating prediction [C]∥Proceedings of 2011 International Conference on Multimedia Technology. Hangzhou: IEEE, 2011: 3405-3408.
[13] ZHAO W X, LI S, HE Y, et al. Connecting social media to e-commerce: cold-start product recommendation using microblogging information [J]. IEEE Transactions on Knowledge & Data Engineering, 2016, 28(5): 1147-1159.
[14] 李洋,陈毅恒,刘挺.微博信息传播预测研究综述[J].软件学报,2016(2): 247-263.
LI Y, CHEN YH, LIU T. Survey on predicting information propagation in microblogs[J]. Journal of Software, 2016,27(2): 247-263.
[15] 赵泽亚,贾岩涛,王元卓,等.大规模演化知识网络中的关联推理[J].计算机研究与发展,2016, 53(2):492-502.
ZHAO ZY, JIA YT, WANG YZ, et al. Link inference in large scale evolutionable knowledge network[J]. Journal of Computer Research and Development, 2016, 53(2): 492-502.
[16] REAFEE W, SALIM N, KHAN A. The power of implicit social relation in rating prediction of social recommender systems [J]. Plos One, 2016, 11(5):120.
[17] PENG F, LU J, WANG Y, et al. N-dimensional markov random field prior for cold-start recommendation [J]. Neurocomputing, 2016, 191(1): 187-199.
[18] MA H, KING I, LYU M R. Learning to recommend with social trust ensemble [C]∥Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Gold Coast, Australia: ACM, 2009: 203-210.
[19] KIM Y A, SONG H S. Strategies for predicting local trust based on trust propagation in social networks [J]. Knowledge-Based Systems, 2011, 24(8): 1360-1371.
[20] YUAN W W, GUAN D H, LEE Y K, et al. Improved trustaware recommender system using small-worldness of trust networks [J]. KnowledgeBased Systems, 2010, 23(3): 232-238.
[21] JIANG W J, WANG G J, WU J. Generating trusted graphs for trust evaluation in online social networks[J]. Future generation computer systems, 2014, 31(1): 48-58.
[22] LIU R R, LIU J G, JIA C X, et al. Personal recommendation via unequal resource allocation on bipartite networks [J]. Physica A: Statistical Mechanics and its Applications, 2010, 389(16): 3282-3289.
[23] GUHA R, KUMAR R, RAGHAVAN P, et al. Propagation of trust and distrust[C]∥ Proceedings Of The 13th International Conference on World Wide Web. New York, USA: ACM, 2004: 403-412.
[24] DENG S, HUANG L, XU G. Social network-based service recommendation with trust enhancement[J]. Expert Systems with Applications, 2014, 41(18):8075-8084.
[25] MORADI P, AHMADIAN S. A reliability-based recommendation method to improve trust-aware recommender systems [J]. Expert Systems with Applications, 2015, 42(21): 7386-7398.
[26] LE H S. Dealing with the new user cold-start problem in recommender systems: A comparative review[J]. Information Systems, 2014, 58(1): 87-104.
[27] WANG Y, YIN G, CAI Z, et al. A trust-based probabilistic recommendation model for social networks[J]. Journal of Network & Computer Applications, 2015, 55(1): 59-67.
[28] WANG Y, CAI Z, YIN G, et al. An Incentive Mechanism with privacy protection in mobile crowdsourcing systems[J]. Computer Networks, 2016, 102(1):157-171. |
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