Advertisement

Probabilistic Attribute Mapping for Cold-Start Recommendation

  • Guangxin Wang
  • Yinglin Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 213)

Abstract

Collaborative filtering recommender system performs well when there are enough historical data of the users’ online behavior, but it does not work on new users who have not rated any items, or new items that have not been rated by any users, which are called cold-start user and cold-start item, respectively. In order to alleviate the cold-start problem, additional information such as the attributes of users and items must be used. We propose a novel hybrid recommender system, which tries to construct the probabilistic relationship between user attributes and movie attributes using EM algorithm. It can make recommendation for both new users and new items. We evaluate our approach on MovieLens dataset and compare our method with the state-of-the-art approach. Experimental results show that the two approaches have almost the same performance, while our approach uses less time to train the model and make online recommendation.

Keywords

Recommender system Cold start User and item features Latent variable model 

Notes

Acknowledgments

This paper was supported by the Science and Technology Innovation Action Plan (Grant Number:12511502902) of Shanghai Science and Technology Committee.

References

  1. 1.
    Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. Internet Comput IEEE 7(1):76–80Google Scholar
  2. 2.
    Miller BN, et al (2003)MovieLens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of 8th international conference on intelligent user interface, ACM 2003Google Scholar
  3. 3.
    Pazzani M, Billsus D (2007) Content-based recommendation systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web. Springer, Berlin, pp 325–341Google Scholar
  4. 4.
    Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Ricci F (ed) Recommender systems handbook, Springer, Berlin, pp 107–144Google Scholar
  5. 5.
    Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of 22nd annual international ACM SIGIR conference on research and development in information retrieval, ACMGoogle Scholar
  6. 6.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Method), 1977:1–38Google Scholar
  7. 7.
    Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Machine Learn 42(1):177–196CrossRefMATHGoogle Scholar
  8. 8.
    Park ST, Chu W (2009) Pairwise preference regression for cold-start recommendation. In: Proceedings of the 3rd ACM conference on recommender systems, ACMGoogle Scholar
  9. 9.
    Schein AI, et al (2002) Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, ACMGoogle Scholar
  10. 10.
    Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval, Vol 1. Cambridge University Press, CambridgeGoogle Scholar
  11. 11.
    Sarwar B, et al (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, ACMGoogle Scholar
  12. 12.
    Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI conference on human factors in computing systemsGoogle Scholar
  13. 13.
    Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  14. 14.
    Koren Y, et al (2009) Matrix factorization techniques for recommender systems. Computer 42:30Google Scholar
  15. 15.
    Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM conference on recommender systems RecSys’10, ACMGoogle Scholar
  16. 16.
    Yehuda Koren. Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, Communications of the ACMGoogle Scholar
  17. 17.
    Zhou K, Yang SH, Zha H (2011) Functional matrix factorizations for cold-start recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, ACMGoogle Scholar
  18. 18.
    Rashid AM, et al (2002) Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th international conference on intelligent user interfaces, ACMGoogle Scholar
  19. 19.
    Golbandi N, Koren Y, Lempel R (2011) Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the 4th ACM international conference on web search and data mining, ACMGoogle Scholar
  20. 20.
    Golbandi N, Koren Y, Lempel R (2010) On bootstrapping recommender systems. In: Proceedings of the 19th ACM international conference on information and knowledge management, ACMGoogle Scholar
  21. 21.
    Popescul A, et al (2001) Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceedings of the 17th conference on uncertainty in artificial intelligenceGoogle Scholar
  22. 22.
    Gantner Z, et al (2010) Learning attribute-to-feature mappings for cold-start recommendations. In: Data mining (ICDM) 2010 IEEE 10th international conferenceGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringShanghai JiaoTong UniversityShanghaiChina

Personalised recommendations