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Augmenting matrix factorization technique with the combination of tags and genres

Author

Listed:
  • Ma, Tinghuai
  • Suo, Xiafei
  • Zhou, Jinjuan
  • Tang, Meili
  • Guan, Donghai
  • Tian, Yuan
  • Al-Dhelaan, Abdullah
  • Al-Rodhaan, Mznah

Abstract

Recommender systems play an important role in our daily life and are becoming popular tools for users to find what they are really interested in. Matrix factorization methods, which are popular recommendation methods, have gained high attention these years. With the rapid growth of the Internet, lots of information has been created, like social network information, tags and so on. Along with these, a few matrix factorization approaches have been proposed which incorporate the personalized information of users or items. However, except for ratings, most of the matrix factorization models have utilized only one kind of information to understand users’ interests. Considering the sparsity of information, in this paper, we try to investigate the combination of different information, like tags and genres, to reveal users’ interests accurately. With regard to the generalization of genres, a constraint is added when genres are utilized to find users’ similar “soulmates”. In addition, item regularizer is also considered based on latent semantic indexing (LSI) method with the item tags. Our experiments are conducted on two real datasets: Movielens dataset and Douban dataset. The experimental results demonstrate that the combination of tags and genres is really helpful to reveal users’ interests.

Suggested Citation

  • Ma, Tinghuai & Suo, Xiafei & Zhou, Jinjuan & Tang, Meili & Guan, Donghai & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2016. "Augmenting matrix factorization technique with the combination of tags and genres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 101-116.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:101-116
    DOI: 10.1016/j.physa.2016.05.021
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    References listed on IDEAS

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    1. Zhang, Zi-Ke & Yu, Lu & Fang, Kuan & You, Zhi-Qiang & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 82-88.
    2. Ding, Chris & Li, Tao & Peng, Wei, 2008. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3913-3927, April.
    3. Zhang, Chu-Xu & Zhang, Zi-Ke & Yu, Lu & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Information filtering via collaborative user clustering modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 195-203.
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    Cited by:

    1. Deng, Xiuqin & Liu, Taiheng & Li, Wenzhou & Liu, Fuchun & Peng, Jiaen, 2019. "A latent factor model of fusing social regularization term and item regularization term," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1330-1342.

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