Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (9): 9-16    DOI: 10.11925/infotech.1003-3513.2015.09.02
Current Issue | Archive | Adv Search |
User Interest Prediction Combing Topic Model and Multi-time Function
Gui Sisi1, Lu Wei1,2, Huang Shihao1, Zhou Pengcheng1
1 School of Information Management, Wuhan University, Wuhan 430072, China;
2 Center for the Studies of Information Resources, Wuhan University, Wuhan 430072, China
Download: PDF(457 KB)   HTML  
Export: BibTeX | EndNote (RIS)      

[Objective] User interest is not static and it changes dynamically as time goes by, this paper proposes a user interest prediction model based on topic model and multi-time function. [Methods] Generate user interests by topic model, and calculate the weights of each user interest at every time point by applying multi-time function in order to predict user interest at next time point. [Results] Compared with memory-based user profile model and multi-step user profile model, cosine similarity and Kullback-Leibler divergence of the experimental results on search engine log data provided by Sogou Lab show that this model can predict user interests more effectively. [Limitations] The proposed method is only tested on search engine log data provided by Sogou Lab, and it need further examination on other data sets. [Conclusions] It is more effective to take every time point of user history data into consideration for user interest prediction.

Received: 03 April 2015      Published: 06 April 2016
:  TP393  

Cite this article:

Gui Sisi, Lu Wei, Huang Shihao, Zhou Pengcheng. User Interest Prediction Combing Topic Model and Multi-time Function. New Technology of Library and Information Service, 2015, 31(9): 9-16.

URL:     OR

[1] 冯子威. 用户兴趣建模的研究[D]. 哈尔滨: 哈尔滨工业大学, 2010. (Feng Ziwei. Research on User Interests Modeling [D]. Harbin: Harbin Institute of Technology, 2010.)
[2] 杨杰, 陈恩红. 面向个性化服务的用户兴趣偏移检测及处理方法[J]. 电子技术, 2009(11): 72-76, 63. (Yang Jie, Chen Enhong. Personalized Service Oriented User Interest Shift Detection and Processing [J]. Electronic Technology, 2009(11):
72-76, 63.)
[3] Ahmed A, Low Y, Aly M, et al. Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting [C]. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2011: 114-122.
[4] Veningston K, Shanmugalakshmi R. Combining User Interested Topic and Document Topic for Personalized Information Retrieval [A]. //Big Data Analytics [M]. Springer International Publishing, 2014: 60-79.
[5] Sakamoto S, Mikawa K, Goto M. A Study on Recommender System Based on Latent Class Model for High Dimensional and Sparse Data [C]. In: Proceedings of the 14th Asia Pacific Industrial Engineering and Management Society Conference, Cebu, Philippines. 2013.
[6] Pennacchiotti M, Gurumurthy S. Investigating Topic Models for Social Media User Recommendation [C]. In: Proceedings of the 20th International Conference Companion on World Wide Web. ACM, 2011: 101-102.
[7] Liu Q, Chen E H, Xiong H, et al. Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2012, 42(1): 218-233.
[8] Mao Q, Feng B, Pan S. Modeling User Interests Using Topic Model [J]. Journal of Theoretical and Applied Information Technology, 2013, 48(1): 600-606.
[9] Ding W, Chen C. Dynamic Topic Detection and Tracking: A Comparison of HDP, C-word, and Cocitation Methods [J]. Journal of the Association for Information Science and Technology, 2014, 65(10): 2084-2097.
[10] Lee T Q, Park Y, Park Y T. A Time-Based Approach to Effective Recommender Systems Using Implicit Feedback [J]. Expert Systems with Applications, 2008, 34(4): 3055-3062.
[11] Lee T Q, Park Y, Park Y T. An Empirical Study on Effectiveness of Temporal Information as Implicit Ratings [J]. Expert Systems with Applications, 2009, 36(2): 1315-1321.
[12] Widmer G, Kubat M. Learning in the Presence of Concept Drift and Hidden Contexts [J]. Machine Learning, 1996, 23(1): 69-101.
[13] 郝水龙, 吴共庆, 胡学钢. 基于层次向量空间模型的用户兴趣表示及更新[J]. 南京大学学报:自然科学版, 2012, 48(2): 190-197. (Hao Shuilong, Wu Gongqing, Hu Xuegang. Presentation and Updation for User Profile Based on Hierarchical Vector Space Model [J]. Journal of Nanjing University: Natural Sciences, 2012, 48(2):190-197.)
[14] 宋丽哲, 牛振东, 余正涛, 等. 一种基于混合模型的用户兴趣漂移方法[J]. 计算机工程, 2006, 32(1): 4-6,89. (Song Lizhe, Niu Zhendong, Yu Zhengtao. A Method of Drifting User's Interests Based on Hybrid Model [J]. Computer Engineering, 2006, 32(1): 4-6,89.)
[15] 布红艳, 王国胤, 董振兴. 邮件系统中的兴趣漂移混合模型[J]. 计算机工程与设计, 2011, 32(12): 4026-4029. (Bu Hongyan, Wang Guoyin, Dong Zhenxing. Hybrid Interest Drifting Model of E-mail Systems [J]. Computer Engineering and Design, 2011,32(12): 4026-4029.)
[16] Maloof M A, Michalski R S. Selecting Examples for Partial Memory Learning [J]. Machine Learning, 2000, 41(1): 27-52.
[17] Koychev I. Gradual Forgetting for Adaptation to Concept Drift [C]. In: Proceedings of ECAI 2000 Workshop on Current Issues in Spatio-Temporal Reasoning, Berlin, Germany. 2000.
[18] Koychev I, Schwab I. Adaptation to Drifting User's Interests [C]. In: Proceedings of ECML2000 Workshop: Machine Learning in New Information Age. 2000: 39-46.
[19] Chen Z, Jiang Y, Zhao Y. A Collaborative Filtering Recommendation Algorithm Based on User Interest Change and Trust Evaluation [J]. International Journal of Digital Content Technology and Its Applications, 2010, 4(9): 106-113.
[20] Zheng N, Li Q. A Recommender System Based on Tag and Time Information for Social Tagging Systems [J]. Expert Systems with Applications, 2011, 38(4): 4575-4587.
[21] Zhang Y, Liu Y. A Collaborative Filtering Algorithm Based on Time Period Partition [C]. In: Proceedings of the 3rd International Symposium on Intelligent Information Technology and Security Informatics, Jinggangshan, China. IEEE, 2010: 777-780.
[22] Karahodza B, Supic H, Donko D. An Approach to Design of Time-Aware Recommender System Based on Changes in Group User's Preferences [C]. In: Proceedings of the 2014 X International Symposium on Telecommunications. IEEE, 2014: 1-4.
[23] Wang Q, Sun M, Xu C. An Improved User-Model-Based Collaborative Filtering Algorithm [J]. Journal of Information and Computational Science, 2011, 8(10): 1837-1846.
[24] 邢春晓, 高凤荣, 战思南, 等. 适应用户兴趣变化的协同过滤推荐算法[J]. 计算机研究与发展, 2007, 44(2): 296-301. (Xing Chunxiao, Gao Fengrong, Zhan Sinan, et al. A Collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change [J]. Journal of Computer Research and Development, 2007, 44(2): 296-301.)
[25] 于洪, 李转运. 基于遗忘曲线的协同过滤推荐算法[J]. 南京大学学报:自然科学版, 2010, 46(5): 520-527. (Yu Hong, Li Zhuanyun. A Collaborative Filtering Recommendation Algorithm Based on Forgetting Curve [J]. Journal of Nanjing University: Natural Sciences, 2010, 46(5): 520-527.)
[26] Wu Y K, Wang Y, Tang Z H. A Collaborative Filtering Recommendation Algorithm Based on Interest Forgetting Curve [J]. International Journal of Advancements in Computing Technology, 2012, 4(10): 148-157.
[27] Liu K, Chen W, Bu J, et al. User Modeling for Recommendation in Blogspace [C]. In: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops. IEEE, 2007: 79-82.
[28] Cheng Y, Qiu G, Bu J, et al. Model Bloggers' Interests Based on Forgetting Mechanism [C]. In: Proceedings of the 17th International Conference on World Wide Web. ACM, 2008: 1129-1130.
[29] Rybak J, Balog K, Nørvåg K. Temporal Expertise Profiling [C]. In: Proceedings of the 36th European Conference on IR Research, Amsterdam, Netherlands. 2014: 540-546.
[30] Wu D, Zhao D, Zhang X. An Adaptive User Profile Based on Memory Model [C]. In: Proceedings of the 9th International Conference on Web-Age Information Management. IEEE, 2008: 461-468.
[31] Wang W, Zhao D, Luo H, et al. Mining User Interests in Web Logs of an Online News Service Based on Memory Model [C]. In: Proceedings of the 8th International Conference on Networking, Architecture and Storage. IEEE, 2013: 151-155.
[32] 于洪涛, 崔瑞飞, 董芹芹. 基于遗忘曲线的微博用户兴趣模型[J]. 计算机工程与设计, 2014, 35(10): 3367-3372, 3379. (Yu Hongtao, Cui Ruifei, Dong Qinqin. Micro-Blog User Interest Model Based on Forgetting Curve [J]. Computer Engineering and Design, 2014, 35(10): 3367-3372, 3379.)
[33] Hofmann T. Probabilistic Latent Semantic Indexing [C]. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1999: 50-57.
[34] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation [J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[35] 崔凯. 基于LDA的主题演化研究与实现[D]. 长沙: 国防科学技术大学, 2010. (Cui Kai. The Research and Implementation of Topic Evolution on LDA [D]. Changsha: National University of Defense Technology, 2010.)
[36] Ding Y, Li X. Time Weight Collaborative Filtering [C]. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. ACM, 2005: 485-492.
[37] Cao J, Xia T, Li J, et al. A Density-Based Method for Adaptive LDA Model Selection [J]. Neurocomputing, 2009, 72(7-9): 1775-1781.
[38] Kullback S, Leibler R A. On Information and Sufficiency [J]. The Annals of Mathematical Statistics, 1951,22(1): 79-86.
[39] Jeong D H, Song M. Time Gap Analysis by the Topic Model-Based Temporal Technique [J]. Journal of Informetrics, 2014, 8(3): 776-790.
[40] Newman D, Asuncion A U, Smyth P, et al. Distributed Algorithms for Topic Models [J]. Journal of Machine Learning Research, 2009, 10: 1801-1828.

[1] Qingtian Zeng,Xiaohui Hu,Chao Li. Extracting Keywords with Topic Embedding and Network Structure Analysis[J]. 数据分析与知识发现, 2019, 3(7): 52-60.
[2] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[3] Yang Ning, Huang Feihu, Wen Yi, Chen Yunwei. An Opinion Evolution Model Based on the Behavior of Micro-blog Users[J]. 现代图书情报技术, 2015, 31(12): 34-41.
[4] Yu Xincong, Li Honglian, Lv Xueqiang. Research on the Application of Hyponymy in the Enrollment Robot[J]. 现代图书情报技术, 2015, 31(12): 65-71.
[5] Wang Zhengjun, Yu Xiaoyi, Jin Yuling. Using Sniffer Technology to Constraint Electronic Resource Excessive Downloading[J]. 现代图书情报技术, 2015, 31(12): 95-100.
[6] Liu Zhanbing, Xiao Shibin. Collaborative Filtering Recommended Algorithm Based on User's Interest Fuzzy Clustering[J]. 现代图书情报技术, 2015, 31(11): 12-17.
[7] Wu Wankun, Wu Qinglie, Gu Jinjiang. Hot Topic Extraction from E-commerce Microblog Based on EM-LDA Integrated Model[J]. 现代图书情报技术, 2015, 31(11): 33-40.
[8] Qiang Shaohua, Wu Peng. The Research of Spatial Measure of Users' Mental Model of Website Category from the View of Regional Differences[J]. 现代图书情报技术, 2015, 31(11): 68-74.
[9] Qin Xuedong. Solution for KVM Private Cloud Management System Based on Drupal[J]. 现代图书情报技术, 2015, 31(11): 91-95.
[10] Wu Jiang, Zhang Jinfan. Research on Follow Influence of Triadic Structure in Social Network——Take Student Relation Network as an Example[J]. 现代图书情报技术, 2015, 31(10): 72-80.
[11] Jiang Chuntao. Automatic Annotation of Bibliographical References in Chinese Patent Documents[J]. 现代图书情报技术, 2015, 31(10): 81-87.
[12] Wang Ying, Zhang Zhixiong, Li Chuanxi, Liu Yi, Tang Yijie, Zhou Zijian, Qian Li, Fu Honghu. The Design and Implementation of Open Engine System for Scientific & Technological Knowledge Organization Systems[J]. 现代图书情报技术, 2015, 31(10): 95-101.
[13] Qin Xiaohui, Le Xiaoqiu. Topic Sources and Trends Tracking Towards Citation Network of Single Paper[J]. 现代图书情报技术, 2015, 31(9): 52-59.
[14] Deng Qiping, Wang Xiaomei. Identifying Influential Authors Based on LeaderRank[J]. 现代图书情报技术, 2015, 31(9): 60-67.
[15] Zheng Haishan. The Automatic System for Infrastructure Deployment in the Data Center of Library[J]. 现代图书情报技术, 2015, 31(9): 97-101.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938