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A Context-Aware Method for Top-k Recommendation in Smart TV

  • Peng Liu
  • Jun Ma
  • Yongjin Wang
  • Lintao Ma
  • Shanshan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)

Abstract

We discuss the video recommendation for smart TV, an increasingly popular media service that provides online videos by TV sets. We propose an effective video recommendation model for smart TV service (RSTV) based on the developed Latent Dirichlet allocation(LDA) to make personalized top-k video recommendation. In addition, we present proper solutions for some critical problems of the smart TV recommender system, such as sparsity problem and contextual computing. Our analysis is conducted using a real world dataset gathered from Hisense smart TV platform, JuHaoKan Video-on-Demand dataset(JHKVoD), which is an implicit watch-log dataset collecting sets of videos watched by each user with their corresponding timestamps. We fully portray our dataset in many respects, and provide details on the experimentation and evaluation framework. Result shows that RSTV performs better comparing to many other baselines. We analyse the influence of some of the parameters as well as the contextual granularity.

Keywords

Context-aware recommendation system Smart TV 

Notes

Acknowledgements

This work was supported by National Key Laboratory, Hisense Co., Ltd., and Natural Science Foundation of China (61272240, 71402083).

References

  1. 1.
    Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 301–304. ACM, New York (2011)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  3. 3.
    Golbeck, J.: Generating predictive movie recommendations from trust in social networks. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 93–104. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272 (2008)Google Scholar
  5. 5.
    Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N., Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 79–86. ACM (2010)Google Scholar
  6. 6.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)Google Scholar
  7. 7.
    Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In: ACM Conference on Recommender Systems, pp. 265–268 (2009)Google Scholar
  8. 8.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887. ACM (2008)Google Scholar
  9. 9.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, vol. 20 (2008)Google Scholar
  10. 10.
    Verstrepen, K., Goethals, B.: Top-N recommendation for shared accounts. In: ACM Conference on Recommender Systems, pp. 59–66 (2015)Google Scholar
  11. 11.
    Yan, X., Guo, J., Lan, Y., Cheng, X.: A biterm topic model for short texts. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1445–1456 (2013)Google Scholar
  12. 12.
    Zheng, Y., Burke, R., Mobasher, B.: Splitting approaches for context-aware recommendation: an empirical study. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 274–279. ACM (2014)Google Scholar
  13. 13.
    Zheng, Y., Mobasher, B., Burke, R., CSLIM: contextual SLIM recommendation algorithms. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 301–304. ACM (2014)Google Scholar
  14. 14.
    Zheng, Y., Mobasher, B., Burke, R., Carskit: a java-based context-aware recommendation engine. In: IEEE International Conference on Data Mining Workshop, pp. 1668–1671 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Peng Liu
    • 1
  • Jun Ma
    • 1
  • Yongjin Wang
    • 2
  • Lintao Ma
    • 2
  • Shanshan Huang
    • 2
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.National Key LaboratoryHisense Co., Ltd.QingdaoChina

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