Please wait a minute...
浙江大学学报(工学版)
计算机技术、控制技术     
基于增量图形模式匹配的动态冷启动推荐方法
张亚楠, 陈德运, 王莹洁, 刘宇鹏
1 .哈尔滨理工大学 计算机科学与技术博士后科研流动站,哈尔滨 150080
2 .哈尔滨理工大学 软件学院,哈尔滨 150040
3. 哈尔滨理工大学 计算机科学与技术学院
4. 烟台大学 计算机与控制工程学院,烟台264000
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
 全文: PDF(1212 KB)   HTML
摘要:

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.

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.
出版日期: 2017-03-06
CLC:  TP 391  
基金资助:

国家自然科学基金青年基金资助项目(61300115,61502410)

作者简介: 张亚楠(1981—), 男, 讲师,博士,从事社交网络推荐等研究.ORCID: 0000-0002-0633-826X. E-mail: ynzhang_1981@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  

引用本文:

张亚楠, 陈德运, 王莹洁, 刘宇鹏. 基于增量图形模式匹配的动态冷启动推荐方法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2017.02.025.

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), 10.3785/j.issn.1008-973X.2017.02.025.

[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 neighborhoodaware 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 trustaware recommender system using small-worldness of trust networks [J]. KnowledgeBased 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.
[1] 郑守国,张勇德,谢文添,樊虎,王青. 基于数字孪生的飞机总装生产线建模[J]. 浙江大学学报(工学版), 2021, 55(5): 843-854.
[2] 张师林,马思明,顾子谦. 基于大边距度量学习的车辆再识别方法[J]. 浙江大学学报(工学版), 2021, 55(5): 948-956.
[3] 宋鹏,杨德东,李畅,郭畅. 整体特征通道识别的自适应孪生网络跟踪算法[J]. 浙江大学学报(工学版), 2021, 55(5): 966-975.
[4] 蔡君,赵罡,于勇,鲍强伟,戴晟. 基于点云和设计模型的仿真模型快速重构方法[J]. 浙江大学学报(工学版), 2021, 55(5): 905-916.
[5] 王虹力,郭斌,刘思聪,刘佳琪,仵允港,於志文. 边端融合的终端情境自适应深度感知模型[J]. 浙江大学学报(工学版), 2021, 55(4): 626-638.
[6] 张腾,蒋鑫龙,陈益强,陈前,米涛免,陈彪. 基于腕部姿态的帕金森病用药后开-关期检测[J]. 浙江大学学报(工学版), 2021, 55(4): 639-647.
[7] 郑英杰,吴松荣,韦若禹,涂振威,廖进,刘东. 基于目标图像FCM算法的地铁定位点匹配及误报排除方法[J]. 浙江大学学报(工学版), 2021, 55(3): 586-593.
[8] 雍子叶,郭继昌,李重仪. 融入注意力机制的弱监督水下图像增强算法[J]. 浙江大学学报(工学版), 2021, 55(3): 555-562.
[9] 于勇,薛静远,戴晟,鲍强伟,赵罡. 机加零件质量预测与工艺参数优化方法[J]. 浙江大学学报(工学版), 2021, 55(3): 441-447.
[10] 胡惠雅,盖绍彦,达飞鹏. 基于生成对抗网络的偏转人脸转正[J]. 浙江大学学报(工学版), 2021, 55(1): 116-123.
[11] 陈杨波,伊国栋,张树有. 基于点云特征对比的曲面翘曲变形检测方法[J]. 浙江大学学报(工学版), 2021, 55(1): 81-88.
[12] 段有康,陈小刚,桂剑,马斌,李顺芬,宋志棠. 基于相位划分的下肢连续运动预测[J]. 浙江大学学报(工学版), 2021, 55(1): 89-95.
[13] 张太恒,梅标,乔磊,杨浩杰,朱伟东. 纹理边界引导的复合材料圆孔检测方法[J]. 浙江大学学报(工学版), 2020, 54(12): 2294-2300.
[14] 梁栋,刘昕宇,潘家兴,孙涵,周文俊,金子俊一. 动态背景下基于自更新像素共现的前景分割[J]. 浙江大学学报(工学版), 2020, 54(12): 2405-2413.
[15] 晋耀,张为. 采用Anchor-Free网络结构的实时火灾检测算法[J]. 浙江大学学报(工学版), 2020, 54(12): 2430-2436.