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Latent Features Based Prediction on New Users’ Tastes

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)

Abstract

Recommendation systems have become popular in recent years. A key challenge in such systems is how to effectively characterize new users’ tastes — an issue that is generally known as the cold-start problem. New users judge the system by the ability to immediately provide them with what they consider interesting. A general method for solving the cold-start problem is to elicit information about new users by having them provide answers to interview questions. In this paper, we present Matrix Factorization K-Means (MFK), a novel method to solve the problem of interview question construction. MFK first learns the latent features of the user and the item through observed rating data and then determines the best interview questions based on the clusters of latent features. We can determine similar groups of users after obtaining the responses to the interview questions. Such recommendation systems can indicate new users’ tastes according to their responses to the interview questions. In our experiments, we evaluate our methods using a public dataset for recommendations. The results show that our method leads to better performance than other baselines.

Keywords

Recommendation System Collaborative Filtering Cold Start 

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References

  1. 1.
    Bobadilla, J., Ortega, F., Hernando, A., Bernal, J.: A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems 26, 225–238 (2012)CrossRefGoogle Scholar
  2. 2.
    Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems, SIGIR 1999 (1999)Google Scholar
  3. 3.
    Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., Schmidt-Thieme, L.: Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. Presented at the Proceedings of the 2010 IEEE International Conference on Data Mining (2010)Google Scholar
  4. 4.
    Golbandi, N., Koren, Y., Lempel, R.: On bootstrapping recommender systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada, pp. 1805–1808 (2010)Google Scholar
  5. 5.
    Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China, pp. 595–604 (2011)Google Scholar
  6. 6.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 61–70 (1992)CrossRefGoogle Scholar
  7. 7.
    Koren, Y., Bell, R., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. Computer 42, 30–37 (2009)CrossRefGoogle Scholar
  8. 8.
    Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing 7, 76–80 (2003)CrossRefGoogle Scholar
  9. 9.
    Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., et al.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, San Francisco, California, USA, pp. 127–134 (2002)Google Scholar
  10. 10.
    Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. SIGKDD Explor. Newsl. 10, 90–100 (2008)CrossRefGoogle Scholar
  11. 11.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, North Carolina, United States, pp. 175–186 (1994)Google Scholar
  12. 12.
    Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, pp. 253–260 (2002)Google Scholar
  13. 13.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. in Artif. Intell. 2009, 2 (2009)CrossRefGoogle Scholar
  14. 14.
    Zhou, K., Yang, S.-H., Zha, H.: Functional matrix factorizations for cold-start recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China, pp. 315–324 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan, R.O.C.

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