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Cold-Start Collaborative Filtering Based on User Registration Process

In: The 19th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Peng-yu Zhu

    (Beihang University)

  • Zhong Yao

    (Beihang University)

Abstract

A key challenge in recommender system research is how to make recommendations to new users. Recently the idea of solving the problem within the context of learning user and item profiles has been proposed. Those methods constructed a decision tree for the initial interview, enabling the recommender to query a user adaptively according to her prior responses. However, those methods have overlooked the new users’ personal attributes. In this paper, we present the method CCFBURP, which constructs an algorithm with two steps, in the first of which we screen neighbors of the target user, using its personal attributes, while in the second of which we train the interview model on the dataset constituted of the neighbors and alternative projects. Then the recommender system forecasts goal of optional project ratings of the target user. Experimental results on the MovieLens dataset demonstrate that the proposed CCFBURP algorithm significantly outperforms existing methods for cold-start recommendation.

Suggested Citation

  • Peng-yu Zhu & Zhong Yao, 2013. "Cold-Start Collaborative Filtering Based on User Registration Process," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), The 19th International Conference on Industrial Engineering and Engineering Management, edition 127, chapter 0, pages 1175-1186, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-38427-1_124
    DOI: 10.1007/978-3-642-38427-1_124
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