JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (2): 71-79.doi: 10.6040/j.issn.1672-3961.1.2016.099

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Exploring user interest expansion method for recommendation

WANG Xin1,2, LU Jingya2, WANG Ying2*   

  1. 1. School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, Jilin, China;
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, Jilin, China
  • Received:2016-03-01 Online:2017-04-20 Published:2016-03-01

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Abstract: An approach of user interest expansion was presented and applied into personal recommendation system, the basic idea was to make some statistics on user's browsing log and clicking log, the user's interest was roughly modelled. The associated relationship from the text similarity, the relevance of language model and potential semantic relationship between the directions of user interest was analyzed, the interest groups using community detection method was identified, the user's interest was enriched appropriately in the same group. By experimental analysis, the impact of user's interest expansion on click rate in personalized recommendations was observed. The click rate had nearly doubled growth.

Key words: personalized recommendation, Infomap search algorithm, interest expansion, map equation, communities detection

CLC Number: 

  • TP391
[1] WANG H B, SHAO S Z, ZHOU X, et al. Preference recommendation for personalized search[J]. Knowledge-Based Systems, 2016(100):124-136.
[2] YU J J, ZHU T Y. Combining long-term and short-term user interest for personalized hashtag recommendation[J]. Frontiers of Computer Science, 2015, 9(4):608-622.
[3] SONG Q, CHENG J, YUAN T, et al. Personalized recommendation meets your next favorite[C] //Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia: ACM, 2015:1775-1778.
[4] 印鉴, 王智圣, 李琪,等. 基于大规模隐式反馈的个性化推荐[J]. 软件学报, 2014,25(9):1953-1966. YIN Jian, WANG Zhisheng, LI Qi, et al. Personalized recommendation based on large-scale implicit feedback[J]. Journal of Software, 2014, 25(9):1953-1966.
[5] 高明, 金澈清, 钱卫宁, 等. 面向微博系统的实时个性化推荐[J]. 计算机学报, 2014(4): 963-975. GAO Ming, JIN Cheqing, QIAN Weining, et al. Real-time and personalized recommendation on microblogging systems[J]. Chinese Journal of Computers, 2014(4):963-975.
[6] 徐建民, 陈振亚, 崔琰. 基于用户兴趣及术语间关系的查询扩展方法[J]. 山东大学学报(理学版), 2011, 46(5):49-53. XU Jianmin, CHEN Zhenya, CUI Yan. Query expansion method based on the user interest and term relation[J]. Journal of Shandong University(Natural Science), 2011, 46(5): 49-53.
[7] MEO D P, QUATTRONE G, URSINO D. A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy[J]. User Modeling and User-Adapted Interaction, 2010, 20(1): 41-86.
[8] 茅琴娇, 冯博琴, 李燕, 等. 一种基于概念格的用户兴趣预测方法[J]. 山东大学学报(工学版), 2010, 40(5): 159-163. MAO Qinjiao, FENG Boqin, LI Yan, et al. A novel users’ interests prediction approach based on concept lattice[J]. Journal of Shandong University(Engineering Science), 2010, 40(5): 159-163.
[9] 杨福强, 王洪国, 董树霞, 等. 基于微博扩展的用户兴趣主题挖掘算法[J]. 计算机工程与设计, 2015(5): 1214-1218. YANG Fuqiang, WANG Hongguo, DONG Shuxia, et al. Topic mining algorithm of user interest based on Weibo extension[J]. Computer Engineering and Design, 2015(5): 1214-1218.
[10] 张艳桃, 王国胤, 于洪. 面向Folksonomy的用户兴趣相似性度量方法[J]. 南京大学学报(自然科学版), 2013, 49(5):588-595. ZHANG Yantao, WANG Guoyin, YU Hong. A users’ interest similarity calculating method in Folksonomy[J]. Journal of Nanjing University(Natural Science), 2013, 49(5):588-595.
[11] 胡吉明, 胡昌平. 基于关系社区发现改进的用户兴趣建模[J]. 情报学报, 2013, 32(7):763-768. HU Jiming, HU Changping. Modeling of users’ preference based on improved discovery of relationship community[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(7):763-768.
[12] YU J J, ZHU T Y. Combining long-term and short-term user interest for personalized hashtag recommendation[J]. Frontiers of Computer Science, 2015, 9(4): 608-622.
[13] FARALLI S, STILO G, VELARDI P. Recommendation of microblog users based on hierarchical interest profiles[J]. Social Network Analysis and Mining, 2015, 5(1):1-23.
[14] XIE H R, LI Q, MAO X D, et al. Community-aware user profile enrichment in folksonomy[J]. Neural Networks, 2014(58): 111-121.
[15] QI L, CHEN E, 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.
[16] LIU Q, CHEN E, XIONG H, et al. Exploiting user interests for collaborative filtering: interests expansion via personalized ranking[C] //Proceedings of the 19th ACM International Conference on Information and Knowledge Management. Toronto, Canada: ACM, 2010:1697-1700.
[17] BEDI P. User interest expansion using spreading activation for generating recommendations[C] //International Conference on Advances in Computing, Communications and Informatics. Kochi, Japan: IEEE, 2015:766-771.
[18] QIAN X, FENG H, ZHAO G, et al. Personalized recommendation combining user interest and social circle[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 1763-1777.
[19] 阚君满. 基于改进哈夫曼编码的全文索引结构压缩算法[J]. 吉林大学学报(信息科学版), 2011, 29(5):473-476. KAN Junman. Compressed format full-text index based on improved Huffman code and its implement [J]. Journal of Jilin University(Information Science Edition), 2011, 29(5):473-476.
[20] HUANG L C, YEN T J, CHOU S C T. Community detection in dynamic social networks: a random walk approach[C] //International Conference on Advances in Social Networks Analysis and Mining. Kaohsiung, China(Taiwan): IEEE, 2011: 110-117.
[21] CHARIKAR M S. Similarity estimation techniques from rounding algorithms[C] //Proceedings of the Thirty-Fourth Annual ACM Symposium on Theory of Computing. New York, USA: ACM, 2002: 380-388.
[22] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C] // Proceedings of the 23th International Conference on Advances in Neural Information Processing Systems. Granada, Spain: MIT Press, 2013: 3111-3119.
[23] 朱亮, 陆静雅, 左万利. 基于用户搜索行为的query-doc关联挖掘[J]. 自动化学报, 2014, 40(8):1654-1666. ZHU Liang, LU Jingya, ZUO Wanli. Query-doc relation mining based on user search behavior [J]. Acta Automatica Sinica, 2014, 40(8):1654-1666.
[24] WU W, LI H, XU J. Learning query and document similarities from click-through bipartite graph with metadata[C] //Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. Rome, Italy: ACM, 2013:687-696.
[25] 岑荣伟, 刘奕群, 张敏, 等. 基于日志挖掘的搜索引擎用户行为分析[J]. 中文信息学报, 2010, 24(3): 49-54. CEN Rongwei, LIU Yiqun, ZHANG Min, et al. Search engine user behavior analysis based on log mining [J]. Journal of Chinese Information Processing, 2010, 24(3): 49-54.
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