Cold-Start Recommendation for On-Demand Cinemas

  • Beibei Li
  • Beihong JinEmail author
  • Taofeng Xue
  • Kunchi Liu
  • Qi Zhang
  • Sihua Tian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11908)


The on-demand cinemas, which has emerged in recent years, provide offline entertainment venues for individuals and small groups. Because of the limitation of network speed and storage space, it is necessary to recommend movies to cinemas, that is, to suggest cinemas to download the recommended movies in advance. This is particularly true for new cinemas. For the new cinema cold-start recommendation, we build a matrix factorization framework and then fuse location categories of cinemas and co-popular relationship between movies in the framework. Specifically, location categories of cinemas are learned through LDA from the type information of POIs around the cinemas and used to approximate cinema latent representations. Moreover, a SPPMI matrix that reflects co-popular relationship between movies is constructed and factorized collectively with the interaction matrix by sharing the same item latent representations. Extensive experiments on real-world data are conducted. The experimental results show that the proposed approach yields significant improvements over state-of-the-art cold-start recommenders in terms of precision, recall and NDCG.


Recommendation system On-demand cinema Cold-start problem Matrix factorization Location category Co-popular relationship 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Beibei Li
    • 1
    • 2
  • Beihong Jin
    • 1
    • 2
    Email author
  • Taofeng Xue
    • 1
    • 2
  • Kunchi Liu
    • 1
    • 2
  • Qi Zhang
    • 3
  • Sihua Tian
    • 3
  1. 1.State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Beijing iQIYI Cinema Management Co., Ltd.BeijingChina

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