JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (1): 15-22.doi: 10.6040/j.issn.1671-9352.1.2015.118

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Information recommendation based on users interest model

YANG Zhen1,2,3, SI Shu-yong1, LI Chao-yang1   

  1. 1. College of Computer Science, Beijing University of Technology, Beijing 100124, China;
    2. Beijing Key Laboratory of Trusted Computing, Beijing 100124, China;
    3. Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
  • Received:2015-11-14 Online:2017-01-20 Published:2017-01-16

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Abstract: Information recommendation technology can help users filtering out useful content from the huge amount of information on the Internet, thus attracts a wide range of researchers attention. In this paper, we proposed a collaborative recommendation algorithm based on the users interest by using latent factor model, which captured the users implicit interests by decompose the User-Behavior matrix into a product of a User-Implicit matrix and an Interest-Behavior matrix. The experimental results in the MovieLens data sets show that the implicit characteristic can reflect the users interest more precisely than explicit characteristics, as a result, improving the recommendation performance as an expectation.

Key words: information recommendation, users interest model, latent factor model, collaborative filtering, personalized recommendation

CLC Number: 

  • TP391
[1] 蔺丰奇, 刘益. 信息过载问题研究述评[J]. 情报理论与实践,2007,30(5):710-714. LIN Fengqi, LIU Yi.Comment on information overload researches[J]. Information Studies: Theory & Application, 2007, 30(5):710-714.
[2] 项亮. 推荐系统实践[M]. 北京:人民邮电出版社,2012.
[3] MILLER B N, ALBERT I, LAM S K, et al. MovieLens unplugged: experiences with an occasionally connected recommender system[C] //Proceedings of the 8th International Conference on Intelligent User Interfaces. New York: ACM, 2003:263-266.
[4] 陈志敏, 李志强. 基于用户特征和项目属性的协同过滤推荐算法[J]. 计算机应用, 2011, 31(7):1748-1750. CHEN Zhimin, LI Zhiqiang. Collaborative filtering recommendation algorithm based on user characteristics and item attributes[J]. Journal of Computer Applications, 2011, 31(7):1748-1750.
[5] RESNICK P, IACOVOU N, SUCHAK M, et al. GroupLens: an open architecture for collaborative filtering of net news[C] //Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. New York: ACM, 1994:175-186.
[6] BELLOGÍN A, CASTELLS P, CANTADOR I. Neighbor selection and weighting in user-based collaborative filtering: a performance prediction approach[J]. ACM Transactions on the Web(TWEB), 2014, 8(2):307-323.
[7] JIA Z, YANG Y, GAO W, et al. User-based collaborative filtering for tourist attraction recommendations[C] //Proceedings of 2015 IEEE International Conference on Computational Intelligence & Communication Technology(CICT). New Jersey: IEEE, 2015: 22-25.
[8] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C] //Proceedings of the 10th International Conference on World Wide Web. New York: ACM, 2001: 285-295.
[9] DESHPANDE M, KARYPIS G. Item-based top-N recommendation algorithms[J]. ACM Transactions on Information Systems. 2004, 22(1): 143-177.
[10] NGUYEN T, HUI M, HARPER M, et al. Exploring the filter bubble: the effect of using recommender systems on content diversity[C] //Proceedings of the 23rd International Conference on World Wide Web. New York: ACM, 2014: 677-686.
[11] ELBADRAWY A, KARYPIS G. User-specific feature-based similarity models for top-n recommendation of new items[J]. ACM Transactions on Intelligent Systems and Technology(TIST), 2015, 9(21):1-20.
[12] HOFF P. Multiplicative latent factor models for description and prediction of social networks[J]. Computational & Mathematical Organization Theory, 2009, 15(4): 261-272.
[13] 鲁权, 王如龙, 张锦, 等. 融合邻域模型与隐语义模型的推荐算法[J].计算机工程与应用, 2013:100-103. LU Quan, WANG Rulong, ZHANG Jin, et al. Recommender algorithm combined with neighborhood model and LFM[J]. Computer Engineering and Applications, 2013:100-103.
[14] SHI Y, LARSON M, HANJALIC A. Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges[J]. ACM Computing Surveys(CSUR), 2014, 47(1): 1-45.
[15] JENATTON R, ROUX N L, BORDES A, et al. A latent factor model for highly multi-relational data[C] //Advances in Neural Information Processing Systems. 2012: 3167-3175.
[16] HARVEY A, RUIZ E, SENTANA E. Unobserved component time series models with ARCH disturbances[J]. Journal of Econometrics, 1992, 52(1): 129-157.
[17] 徐翔, 王煦法. 协同过滤算法中的相似度优化方法[J]. 计算机工程, 2010, 36(06): 52-54. XU Xiang, WANG Xufa. Optimization method of similarity degree in collaborative filter algorithm[J]. Computer Engineering, 2010, 36(06): 52-54.
[18] BURGES C, SHAKED T, RENSHAW E, et al. Learning to rank using gradient descent[C] //Proceedings of the 22nd International Conference on Machine Learning. New York: ACM, 2005: 89-96.
[19] SARWAR B M, KONSTAN J A, BORCHERS A, et al. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system[C] //Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work. New York:ACM, 1998: 345-354.
[20] 刘学艺, 李平, 郜传厚. 极限学习机的快速留一交叉验证算法[J]. 上海交通大学学报, 2011, 45(8): 1140-1145. LIU Xueyi, LI Ping, GAO Chuanhou. Fast leave-one-out cross-validation algorithm for extreme learning machine[J]. Journal of Shanghai Jiaotong University, 2011, 45(8):1140-1145.
[21] 刘建国, 周涛, 郭强. 个性化推荐系统评价方法综述 [J]. 复杂系统与复杂性科学, 2009, 6(3): 1-10. LIU Jianguo, ZHOU Tao, GUO Qiang, et al. Overview of the evaluated algorithms for the personal recommendation systems[J]. Complex Systems and Complexity Science, 2009, 6(3): 1-10.
[22] 朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2): 163-175. ZHU Yuxiao, L(¨overU)Linyuan. Evaluation metrics for recommender systems[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(2): 163-175.
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