PLSA Collaborative Filtering Algorithm Incorporated with User Interest Change
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    Abstract:

    Recommend system is an effective method for people to get useful knowledge from mass information. It has attracted widespread attention in both academia and industry. Collaborative filtering (CF) is the most popular algorithm in the research of Recommend system. However most of current CF algorithms are static models, which do not take into account of user interest changing. The paper proposed a hybrid recommend method, which capture user's long-term interests with Gaussian probabilistic latent semantic (PLSA) algorithm, at the same time, capture user's short-time interests with rating window. The experimental results obtained on Movielens dataset and Netflix dataset clearly show that the new algorithm is more accurate than traditional user-based algorithm and PLSA algorithm.

    Reference
    1 刘建国,周涛,汪秉宏.个性化推荐系统的研究进展.自然科学进展,2009,19(1):1-15.
    2 曹毅.基于内容和协同过滤的混合模式推荐技术研究[学位论文].长沙:中南大学,2007.
    3 Sarwar B, Karypis G. Item-based collaborative filtering recommendation algorithms. WWW2007,285-295.
    4 Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. IEEE Computer Society, 2009, 42(8): 30-37.
    5 Miyahara K, Pazzani MJ. Collaborative filtering with the simple bayesian classifier. 6th Pacific Rim International Conference on Artificial Intelligence. 2000, 1886. 679-689.
    6 Hofmann T. Latent class models for collaborative filtering. Proc. of the International Joint Conference in Artificial Intelligence. 1999. 688-693.
    7 王卫平,刘颖.基于客户行为序列的推荐算法.计算机系统应用, 2006,15(9):35-38.
    8 Hofmann T. Latent semantic models for collaborative filtering. ACM Transactions (TOIS), 2004, 22(1): 89-115.
    9 Liang X, Yuan Q, Zhao S, Chen L, Zhang X, Yang Q, Sun J. Temporal recommendation on graphs via long- and short-term preference fusion. SIGKDD. 2010.723-732.
    10 丁建勇.基于时间特性的网络结构推荐[学位论文].成都:电子科技大学,2009.
    11 顾申华.结合奇异值分解和时间权重的协同过滤算法.计算机应用与软件,2010,27(6):256-259.
    12 陈登科,孔繁胜.基于高斯pLSA模型与项目的协同过滤混合推荐.计算机工程与应用,2010,46(23):209-234.
    13 Herlocker JL, Konstan JA, et al. Evaluating collaborative filtering recommender systems. ACM Trans. (TOIS), 2004, 20(1): 5-53.
    14 Hofmann T. Collaborative filtering via gaussian probabi- listic latent semantic analysis. Proc. of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. 2003. 259-266.
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吴成超,王卫平.考虑用户兴趣变化的概率隐语意协同推荐算法.计算机系统应用,2014,23(5):162-166

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History
  • Received:September 28,2013
  • Revised:October 24,2013
  • Online: May 29,2014
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