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A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains

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  • Ramezani, Mohsen
  • Moradi, Parham
  • Akhlaghian, Fardin

Abstract

Recommender systems seek to find the interesting items by filtering out the worthless items. Collaborative filtering is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items and recommends the items which are welcomed by others in the group to the user. But, many challenges like sparsity and computational issues still arise. In this paper, to overcome these challenges, we propose a novel method to find the neighbor users based on the users’ interest patterns. The main idea is that users who are interested in the same set of items share similar interest patterns. Therefore, the non-redundant item subspaces are extracted to indicate the different patterns of interest. Then, a user’s tree structure is created based on the patterns he has in common with the active user. Moreover, a novel recommendation method is presented to predict a new rating value for unseen items. Experimental results on the Movielens and the Jester datasets show that in most cases, the proposed method gains better results than already widely used methods.

Suggested Citation

  • Ramezani, Mohsen & Moradi, Parham & Akhlaghian, Fardin, 2014. "A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 72-84.
  • Handle: RePEc:eee:phsmap:v:408:y:2014:i:c:p:72-84
    DOI: 10.1016/j.physa.2014.04.002
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    References listed on IDEAS

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    1. Shang, Ming-Sheng & Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Collaborative filtering with diffusion-based similarity on tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1259-1264.
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    Citations

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    Cited by:

    1. Geng, Bingrui & Li, Lingling & Jiao, Licheng & Gong, Maoguo & Cai, Qing & Wu, Yue, 2015. "NNIA-RS: A multi-objective optimization based recommender system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 383-397.
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    3. Hu, Liang & Ren, Liang & Lin, Wenbin, 2018. "A reconsideration of negative ratings for network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 690-701.
    4. Rashidi, Rahim & Khamforoosh, Keyhan & Sheikhahmadi, Amir, 2020. "An analytic approach to separate users by introducing new combinations of initial centers of clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    5. Maihami, Vafa & Zandi, Danesh & Naderi, Kasra, 2019. "Proposing a novel method for improving the performance of collaborative filtering systems regarding the priority of similar users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    6. Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.
    7. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.

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