ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

A posts recommendation method based on the collaborative filtering and PageRank

  • 1.

    School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China

  • 2.

    Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology, Kunming 650504, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.07.006
  • Received Date: 21 March 2014
  • Accepted Date: 15 June 2014
  • Rev Recd Date: 15 June 2014
  • Publish Date: 30 July 2014
    Abstract

  • Abstract

    In order to solve the problem of information overload in the post bar, a method of information filtration was proposed based on the users commenting behavior. After analyzing the properties of the recommended posts, the importance of an individual user was evaluated by the PageRank algorithm, in which the weight of replies to the posts among users and the weight of reply intervals were taken into consideration. The users with a high PageRank score were then taken as a cluster center in k-means clustering. The similarity between two groups of users (one from the clustering analysis and the other from the recommending system) was calculated by a collaborative filtering algorithm. The posts with high correlations to the users were presented as the recommended results. Experimental results show that the proposed method performs better than the recommending methods in use.

    Abstract

    In order to solve the problem of information overload in the post bar, a method of information filtration was proposed based on the users commenting behavior. After analyzing the properties of the recommended posts, the importance of an individual user was evaluated by the PageRank algorithm, in which the weight of replies to the posts among users and the weight of reply intervals were taken into consideration. The users with a high PageRank score were then taken as a cluster center in k-means clustering. The similarity between two groups of users (one from the clustering analysis and the other from the recommending system) was calculated by a collaborative filtering algorithm. The posts with high correlations to the users were presented as the recommended results. Experimental results show that the proposed method performs better than the recommending methods in use.
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    • References(11)
  • [1]
    Li Q, Wang J, Peter Chen Y Z, et al. User comments for news recommendation in forum-based social media[J]. Information Sciences, 2010, 180(24): 4 929-4 939.
    [2]
    Somlo G, Howe A E. Adaptive lightweight text filtering[C]// Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis. London, UK: Springer, 2001: 319-329.
    [3]
    Zhang Y, Callan J, Minka T. Novelty and redundancy detection in adaptive filtering[C]// Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Tampere, Finland: ACM Press, 2002: 81-88.
    [4]
    Zhou T, Ren J, Medo M, et al. Bipartite network projection and personal recommendation[J]. Physical Review E, 2007, 76(4): 215-226.
    [5]
    Zhou T, Jiang L L, Su R Q, et al. Effect of initial configuration on network-based recommendation[J].Europhys Lett, 2008, 81(5): 729-736.
    [6]
    Huang Z, Chen H, Zeng D D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering[J]. IEEE Transactions on Information Systems, 2004, 22(1): 116-142.
    [7]
    Huang Z, Zeng D D, Chen H. Analyzing consumer-product graphs: Empirical findings and applications in recommender systems[J]. Management Science, 2007, 53(7): 1 146-1 164.
    [8]
    Su X Y, Khoshgoftaar T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, No.4: 1-19.
    [9]
    McLaughlin M R, Herlocker J L. A collaborative filtering algorithm and evaluation metric that accurately model the user experience[C]// Proceedings of 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Sheffield, UK: ACM Press, 2004: 329-336.
    [10]
    张新猛, 蒋盛益. 基于协同过滤的网络论坛个性化推荐算法[J]. 计算机工程, 2012, 38(5): 67-69.
    [11]
    Shardanand U, Maes P. Social information filtering: Algorithms for automating “word of mouth”[C]// Proceedings of the SIGCHI conference on Human Factors in Computing Systems. Chicago USA: ACM Press, 1995: 210-217.
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    [1]
    Li Q, Wang J, Peter Chen Y Z, et al. User comments for news recommendation in forum-based social media[J]. Information Sciences, 2010, 180(24): 4 929-4 939.
    [2]
    Somlo G, Howe A E. Adaptive lightweight text filtering[C]// Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis. London, UK: Springer, 2001: 319-329.
    [3]
    Zhang Y, Callan J, Minka T. Novelty and redundancy detection in adaptive filtering[C]// Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Tampere, Finland: ACM Press, 2002: 81-88.
    [4]
    Zhou T, Ren J, Medo M, et al. Bipartite network projection and personal recommendation[J]. Physical Review E, 2007, 76(4): 215-226.
    [5]
    Zhou T, Jiang L L, Su R Q, et al. Effect of initial configuration on network-based recommendation[J].Europhys Lett, 2008, 81(5): 729-736.
    [6]
    Huang Z, Chen H, Zeng D D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering[J]. IEEE Transactions on Information Systems, 2004, 22(1): 116-142.
    [7]
    Huang Z, Zeng D D, Chen H. Analyzing consumer-product graphs: Empirical findings and applications in recommender systems[J]. Management Science, 2007, 53(7): 1 146-1 164.
    [8]
    Su X Y, Khoshgoftaar T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, No.4: 1-19.
    [9]
    McLaughlin M R, Herlocker J L. A collaborative filtering algorithm and evaluation metric that accurately model the user experience[C]// Proceedings of 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Sheffield, UK: ACM Press, 2004: 329-336.
    [10]
    张新猛, 蒋盛益. 基于协同过滤的网络论坛个性化推荐算法[J]. 计算机工程, 2012, 38(5): 67-69.
    [11]
    Shardanand U, Maes P. Social information filtering: Algorithms for automating “word of mouth”[C]// Proceedings of the SIGCHI conference on Human Factors in Computing Systems. Chicago USA: ACM Press, 1995: 210-217.
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