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CBPF: Leveraging Context and Content Information for Better Recommendations

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Advanced Data Mining and Applications (ADMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11323))

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Abstract

Recommender systems (RS) help users to find their appropriate items among large volumes of information. Among the different types of RS, context-aware recommender systems aim at personalizing as much as possible the recommendations based on the context situation in which the user is. In this paper we present an approach integrating contextual information into the recommendation process by modeling either item-based or user-based influence of the context on ratings, using the Pearson Correlation Coefficient. The proposed solution aims at taking advantage of content and contextual information in the recommendation process. We evaluate and show effectiveness of our approach on three different contextual datasets and analyze the performances of the variants of our approach based on the characteristics of these datasets, especially the sparsity level of the input data and amount of available information.

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References

  1. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. (TOIS) 23, 103–145 (2005)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_7

    Chapter  MATH  Google Scholar 

  3. Baltrunas, L., et al.: InCarMusic: context-aware music recommendations in a car. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 89–100. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23014-1_8

    Chapter  Google Scholar 

  4. Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: RecSys, pp. 301–304. ACM (2011)

    Google Scholar 

  5. Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Cohen, I., Huang, Y., Chen, J., Benesty, J. (eds.) Noise Reduction in Speech Processing. STSP, vol. 2, pp. 1–4. Springer, Berlin (2009). https://doi.org/10.1007/978-3-642-00296-0_5

    Chapter  Google Scholar 

  6. Braunhofer, M., Elahi, M., Ricci, F., Schievenin, T.: Context-aware points of interest suggestion with dynamic weather data management. In: Xiang, Z., Tussyadiah, I. (eds.) Information and Communication Technologies in Tourism 2014, pp. 87–100. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03973-2_7

    Chapter  Google Scholar 

  7. Codina, V., Ricci, F., Ceccaroni, L.: Distributional semantic pre-filtering in context-aware recommender systems. User Model. User-Adap. Inter. 26, 1–32 (2016)

    Article  Google Scholar 

  8. Guo, G., Zhang, J., Sun, Z., Yorke-Smith, N.: LibRec: a Java library for recommender systems. In: UMAP Workshops (2015)

    Google Scholar 

  9. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010)

    Book  Google Scholar 

  10. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  11. Košir, A., Odic, A., Kunaver, M., Tkalcic, M., Tasic, J.F.: Database for contextual personalization. Elektrotehniški vestnik 78, 270–274 (2011)

    Google Scholar 

  12. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_1

    Chapter  MATH  Google Scholar 

  13. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in artificial intelligence (2009)

    Article  Google Scholar 

  14. Vahidi Ferdousi, Z., Colazzo, D., Negre, E.: CBPF: leveraging context and content information for better recommendations. arXiv preprint arXiv:1810.00751 (2018)

  15. Vahidi Ferdousi, Z., Colazzo, D., Negre, E.: Correlation-based pre-filtering for context-aware recommendation. In: PerCom Workshops (CoMoRea). IEEE (2018)

    Google Scholar 

  16. Zheng, Y., Burke, R., Mobasher, B.: Optimal feature selection for context-aware recommendation using differential relaxation. In: ACM RecSys 2012 (2012)

    Google Scholar 

  17. Zheng, Y., Mobasher, B., Burke, R.: CARSKIT: a Java-based context-aware recommendation engine. In: Proceedings of the 15th IEEE ICDM Workshops (2015)

    Google Scholar 

  18. Zheng, Y., Mobasher, B., Burke, R.: Similarity-based context-aware recommendation. In: Wang, J., et al. (eds.) WISE 2015. LNCS, vol. 9418, pp. 431–447. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26190-4_29

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Paris-Dauphine University, PSL Research University, CNRS UMR 7243, LAMSADE, Paris, France

    Zahra Vahidi Ferdousi, Dario Colazzo & Elsa Negre

Authors
  1. Zahra Vahidi Ferdousi
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  2. Dario Colazzo
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  3. Elsa Negre
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Corresponding author

Correspondence to Zahra Vahidi Ferdousi .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Guojun Gan

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  3. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

  4. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Vahidi Ferdousi, Z., Colazzo, D., Negre, E. (2018). CBPF: Leveraging Context and Content Information for Better Recommendations. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_32

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  • DOI: https://doi.org/10.1007/978-3-030-05090-0_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05089-4

  • Online ISBN: 978-3-030-05090-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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