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A novel combination recommendation method for solving sparse and cold start problems

  • 1.

    Mathematics & Information Science Colledge, Hebei Normal University, Shijiazhuang 050024, China

  • 2.

    College of Hmanities & Information, Changchun University Of Technology, Changchun 130000, China

  • 3.

    School of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.10.002
  • Received Date: 27 August 2015
  • Accepted Date: 29 September 2015
  • Rev Recd Date: 29 September 2015
  • Publish Date: 30 October 2015
    Abstract

  • Abstract

    Considering the problems resulting from the traditional recommended approaches which are powerless to address the well-known cold-start and data sparseness, and the fact that most currently existing association rule mining(ARM) algorithms were designed with basket-oriented analysis in mind, which are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user, this paper introduces a novel association recommendation method based on combination similarity, and proposes a solution to the cold start problem by combining association rules and collaborative filtering techniques. The proposed method focuses on mining rules for only one target user or target item at a time, while utilizing the interest factor to balance the weight between active users (or items) and non active users (or items), which in order to recommend an optimal solution (rules) via weighted method. To recommend both high ratings and collection of items with high similarity, the similarity measurement method was used to filter low similarity items, and to provide the final results by combining the association rules and CF recommendation, realizing user-based or item-based collaborative filtering recommendation. Experiments on the MovieLens data set reveals that the results obtained from employing this method has significantly better than the publishecl results and that it is better able to deal with sparse data and cold start problems.

    Abstract

    Considering the problems resulting from the traditional recommended approaches which are powerless to address the well-known cold-start and data sparseness, and the fact that most currently existing association rule mining(ARM) algorithms were designed with basket-oriented analysis in mind, which are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user, this paper introduces a novel association recommendation method based on combination similarity, and proposes a solution to the cold start problem by combining association rules and collaborative filtering techniques. The proposed method focuses on mining rules for only one target user or target item at a time, while utilizing the interest factor to balance the weight between active users (or items) and non active users (or items), which in order to recommend an optimal solution (rules) via weighted method. To recommend both high ratings and collection of items with high similarity, the similarity measurement method was used to filter low similarity items, and to provide the final results by combining the association rules and CF recommendation, realizing user-based or item-based collaborative filtering recommendation. Experiments on the MovieLens data set reveals that the results obtained from employing this method has significantly better than the publishecl results and that it is better able to deal with sparse data and cold start problems.
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    • References(29)
  • [1]
    Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J].Communications of ACM, 1992, 35(12): 61-70.
    [2]
    Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1): 76-80.
    [3]
    Su X Y, Khoshgoftaar T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, 4: 1-19.
    [4]
    Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
    [5]
    Su X Y, Khoshgoftaar T M. Collaborative filtering for multi-class data using belief nets algorithms[C]// Proceedings of the International Conference on Tools with Artificial Intelligence. Arlington, USA: IEEE Computer Society, 2006: 497-504.
    [6]
    Yu K, Schwaighofer A, Tresp V, et al. Probabilistic memory-based collaborative filtering[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(1): 56-69.
    [7]
    Ben J, Dan F, Jon H. The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin Heidelberg: Springer, 2004.
    [8]
    Lü L, Medo M, et al. Recommender systems[J]. Physics Reports, 2012, 519(1): 1-49.
    [9]
    Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
    [10]
    Huang Z, Zeng D, Chen H. A comparative study of recommendation algorithms in e-commerce applications[J]. IEEE Intelligent Systems, 2007, 22(5): 68-78.
    [11]
    García E, Romero C, Ventura S, et al. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering[J].User Modeling and User-Adapted Interaction, 2009, 19(1-2): 99-132.
    [12]
    Sarwar B, Karypis G, Konstan J, et al. Analysis of recommendation algorithms for E-commerce[C]// Proceedings of the ACM E-Commerce. NewYork, USA: ACM Press, 2000: 158-167.
    [13]
    Leung C W K, Chan S C F, Chung F L. A collaborative filtering framework based on fuzzy association rules and multi-level similarity[J]. Knowledge and Information Systems, 2006, 10(3): 357-381.
    [14]
    Leung C W K, Chan S C F, Chung F L. Applying cross-level association rule mining to cold-start recommendations[C]// Proceeding of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops. Silicon Valley, USA: IEEE Press, 2007:133-136.
    [15]
    Leung C W K, Chan S C F, Chung F L. An empirical study of a cross-level association rule mining approach to cold-start recommendations[J]. Knowledge-Based Systems, 2008, 21(7): 515-529.
    [16]
    Lin W, Alvarez S A, Ruiz C. Efficient adaptive-support association rule mining for recommender systems[J]. Data Mining and Knowledge Discovery, 2014, 6(1): 83-105.
    [17]
    Shaw G, Xu Y, Geva S. Using association rules to solve the cold-start problem in recommender systems[C]// Proceeding of the 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Berlin: Springer, 2014: 340-347.
    [18]
    Sobhanam H, Mariappan A K. Addressing cold start problem in recommender systems using association rules and clustering technique[C]// Proceeding of the International Conference on Computer Communication and Informatics. Coimbatore: IEEE press, 2013:1-5.
    [19]
    Khanzadeh Z, Mahdavi M. Utilizing association rules for improving the performance of collaborative filtering[J]. International Journal of E-Entrepreneurship and Innovation, 2012, 3(2): 14-28.
    [20]
    Tyagi S, Bharadwaj K K. Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining[J]. Swarm and Evolutionary Computation, 2013, 13: 1-12.
    [21]
    Tyagi S, Bharadwaj K K. Enhanced new user recommendations based on quantitative association rule mining[J]. Procedia Computer Science, 2012, 10: 102-109.
    [22]
    Ye H W. A personalized collaborative filtering recommendation using association rules mining and self-organizing map[J]. Journal of Software, 2011, 6(4): 732-739.
    [23]
    Yang H. Improved collaborative filtering recommendation algorithm based on weighted association rules[J]. Applied Mechanics and Materials, 2013, (411-414): 94-97.
    [24]
    郭晓波, 赵书良, 王长宾, 等. 一种新的面向普通用户的多值属性关联规则可视化挖掘方法[J]. 电子学报, 2015, 43(2): 344-352.
    Guo X B, Zhao S L, Wang C B, et al. A new visualizing mining method of Multi-valued attribute association rules for ordinary users[J]. Acta Electronica Sinica, 2015, 43(23): 344-352.
    [25]
    Sarwar B M, Karypis G, Konstan J A, et al. Item-based collaborative filtering recommendation algorithms[C]// Proceedings of the 10th International Conference on World Wide Web. New York, USA: ACM Press, 2001: 285-295.
    [26]
    Breese J, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]// Proceeding of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1998: 43-52.
    [27]
    MovieLens Dataset[EB/OL]. http://www.grouplens.org/datasets/movielens/.
    [28]
    Koren Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model[C]// Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA: ACM Press, 2008: 426-434.
    [29]
    Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering[C]// Proceedings of the 24th International Conference on Machine learning. Corvallis, USA: ACM Press, 2007: 791-798.)
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    [1]
    Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J].Communications of ACM, 1992, 35(12): 61-70.
    [2]
    Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1): 76-80.
    [3]
    Su X Y, Khoshgoftaar T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, 4: 1-19.
    [4]
    Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
    [5]
    Su X Y, Khoshgoftaar T M. Collaborative filtering for multi-class data using belief nets algorithms[C]// Proceedings of the International Conference on Tools with Artificial Intelligence. Arlington, USA: IEEE Computer Society, 2006: 497-504.
    [6]
    Yu K, Schwaighofer A, Tresp V, et al. Probabilistic memory-based collaborative filtering[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(1): 56-69.
    [7]
    Ben J, Dan F, Jon H. The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin Heidelberg: Springer, 2004.
    [8]
    Lü L, Medo M, et al. Recommender systems[J]. Physics Reports, 2012, 519(1): 1-49.
    [9]
    Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
    [10]
    Huang Z, Zeng D, Chen H. A comparative study of recommendation algorithms in e-commerce applications[J]. IEEE Intelligent Systems, 2007, 22(5): 68-78.
    [11]
    García E, Romero C, Ventura S, et al. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering[J].User Modeling and User-Adapted Interaction, 2009, 19(1-2): 99-132.
    [12]
    Sarwar B, Karypis G, Konstan J, et al. Analysis of recommendation algorithms for E-commerce[C]// Proceedings of the ACM E-Commerce. NewYork, USA: ACM Press, 2000: 158-167.
    [13]
    Leung C W K, Chan S C F, Chung F L. A collaborative filtering framework based on fuzzy association rules and multi-level similarity[J]. Knowledge and Information Systems, 2006, 10(3): 357-381.
    [14]
    Leung C W K, Chan S C F, Chung F L. Applying cross-level association rule mining to cold-start recommendations[C]// Proceeding of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops. Silicon Valley, USA: IEEE Press, 2007:133-136.
    [15]
    Leung C W K, Chan S C F, Chung F L. An empirical study of a cross-level association rule mining approach to cold-start recommendations[J]. Knowledge-Based Systems, 2008, 21(7): 515-529.
    [16]
    Lin W, Alvarez S A, Ruiz C. Efficient adaptive-support association rule mining for recommender systems[J]. Data Mining and Knowledge Discovery, 2014, 6(1): 83-105.
    [17]
    Shaw G, Xu Y, Geva S. Using association rules to solve the cold-start problem in recommender systems[C]// Proceeding of the 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Berlin: Springer, 2014: 340-347.
    [18]
    Sobhanam H, Mariappan A K. Addressing cold start problem in recommender systems using association rules and clustering technique[C]// Proceeding of the International Conference on Computer Communication and Informatics. Coimbatore: IEEE press, 2013:1-5.
    [19]
    Khanzadeh Z, Mahdavi M. Utilizing association rules for improving the performance of collaborative filtering[J]. International Journal of E-Entrepreneurship and Innovation, 2012, 3(2): 14-28.
    [20]
    Tyagi S, Bharadwaj K K. Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining[J]. Swarm and Evolutionary Computation, 2013, 13: 1-12.
    [21]
    Tyagi S, Bharadwaj K K. Enhanced new user recommendations based on quantitative association rule mining[J]. Procedia Computer Science, 2012, 10: 102-109.
    [22]
    Ye H W. A personalized collaborative filtering recommendation using association rules mining and self-organizing map[J]. Journal of Software, 2011, 6(4): 732-739.
    [23]
    Yang H. Improved collaborative filtering recommendation algorithm based on weighted association rules[J]. Applied Mechanics and Materials, 2013, (411-414): 94-97.
    [24]
    郭晓波, 赵书良, 王长宾, 等. 一种新的面向普通用户的多值属性关联规则可视化挖掘方法[J]. 电子学报, 2015, 43(2): 344-352.
    Guo X B, Zhao S L, Wang C B, et al. A new visualizing mining method of Multi-valued attribute association rules for ordinary users[J]. Acta Electronica Sinica, 2015, 43(23): 344-352.
    [25]
    Sarwar B M, Karypis G, Konstan J A, et al. Item-based collaborative filtering recommendation algorithms[C]// Proceedings of the 10th International Conference on World Wide Web. New York, USA: ACM Press, 2001: 285-295.
    [26]
    Breese J, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]// Proceeding of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1998: 43-52.
    [27]
    MovieLens Dataset[EB/OL]. http://www.grouplens.org/datasets/movielens/.
    [28]
    Koren Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model[C]// Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA: ACM Press, 2008: 426-434.
    [29]
    Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering[C]// Proceedings of the 24th International Conference on Machine learning. Corvallis, USA: ACM Press, 2007: 791-798.)
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