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European Conference on Information Retrieval

ECIR 2019: Advances in Information Retrieval pp 337–342Cite as

Learning User and Item Representations for Recommender Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11438))

Abstract

The fields of Recommender Systems (RS) and Information Retrieval (IR) are closely related. A Recommender System can usually be seen as a specialized Information Retrieval system where the information need is implicit in the user profile. This parallelism has been exploited in the past to transfer methods between fields. One popular approach is to put the standard bag-of-words representation of queries and documents in IR at the same level as the user and item representations obtained from the user-item matrix in RS. Furthermore, in the last years, new ways of representing words and documents as densely distributed representations have risen. These embeddings show the ability to capture the syntactic and semantic relationships of words and have been applied both in IR and natural language processing. It is our objective to study ways to adapt those techniques to produce user/item representations, evaluate their quality and find ways to exploit them to make useful recommendations. Moreover, we will study ways to generate those representations leveraging properties particular to collaborative filtering data.

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References

  1. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd edn. Addison-Wesley Publishing Company, Boston (2011)

    Google Scholar 

  2. Bellogín, A., Castells, P., Cantador, I.: Precision-oriented evaluation of recommender systems. In: Proceedings of the 5th ACM Conference on Recommender Systems, RecSys 2011, pp. 333–336. ACM (2011). https://doi.org/10.1145/2043932.2043996

  3. Bellogín, A., Castells, P., Cantador, I.: Statistical biases in information retrieval metrics for recommender systems. Inf. Retrieval J. 20(6), 606–634 (2017). https://doi.org/10.1007/s10791-017-9312-z

    Article  Google Scholar 

  4. Bellogín, A., Wang, J., Castells, P.: Text retrieval methods for item ranking in collaborative filtering. In: Clough, P., et al. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 301–306. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20161-5_30

    Chapter  Google Scholar 

  5. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46. ACM (2010). https://doi.org/10.1145/1864708.1864721

  6. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990). https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

    Article  Google Scholar 

  7. Firth, J.R.: A synopsis of linguistic theory 1930–55. In: Studies in Linguistic Analysis (Special Volume of the Philological Society), vol. 1952–59, pp. 1–32. The Philological Society, Oxford (1957)

    Google Scholar 

  8. Furnas, G.W., et al.: Information retrieval using a singular value decomposition model of latent semantic structure. In: Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1988, pp. 465–480. ACM (1988). https://doi.org/10.1145/62437.62487

  9. Ganguly, D., Roy, D., Mitra, M., Jones, G.J.: Word embedding based generalized language model for information retrieval. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015, pp. 795–798. ACM (2015). https://doi.org/10.1145/2766462.2767780

  10. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272, December 2008. https://doi.org/10.1109/ICDM.2008.22

  11. Landin, A., Valcarce, D., Parapar, J., Barreiro, Á.: PRIN: a probabilistic recommender with item priors and neural models. In: Azzopardi, L., et al. (eds.) ECIR 2019. LNCS, vol. 11438, pp. 133–147. Springer, Heidelberg (2019)

    Google Scholar 

  12. Lavrenko, V., Croft, W.B.: Relevance based language models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2001, pp. 120–127. ACM (2001). https://doi.org/10.1145/383952.383972

  13. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning, vol. 32, pp. 1188–1196 (2014)

    Google Scholar 

  14. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR (abs/1301.3), January 2013

    Google Scholar 

  15. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, NIPS 2013, vol. 26, pp. 3111–3119 (2013)

    Google Scholar 

  16. Mitra, B., Craswell, N.: An introduction to neural information retrieval. Found. Trends® Inf. Retrieval 13(1), 1–126 (2018). https://doi.org/10.1561/1500000061

    Article  Google Scholar 

  17. Nalisnick, E., Mitra, B., Craswell, N., Caruana, R.: Improving document ranking with dual word embeddings. In: Proceedings of the 25th International Conference Companion on World Wide Web, WWW 2016 Companion, International World Wide Web Conferences Steering Committee, pp. 83–84 (2016). https://doi.org/10.1145/2872518.2889361

  18. Ning, X., Karypis, G.: SLIM: sparse linear methods for top-n recommender systems. In: 2011 IEEE 11th International Conference on Data Mining, pp. 497–506, December 2011. https://doi.org/10.1109/ICDM.2011.134

  19. Parapar, J., Bellogín, A., Castells, P., Barreiro, A.: Relevance-based language modelling for recommender systems. Inf. Process. Manag. 49(4), 966–980 (2013). https://doi.org/10.1016/j.ipm.2013.03.001

    Article  Google Scholar 

  20. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics (2014). https://doi.org/10.3115/v1/D14-1162

  21. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461. AUAI Press (2009)

    Google Scholar 

  22. Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook, 2nd edn. Springer, Heidelberg (2015)

    Book  Google Scholar 

  23. Valcarce, D., Bellogín, A., Parapar, J., Castells, P.: On the robustness and discriminative power of information retrieval metrics for top-n recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, pp. 260–268. ACM (2018). https://doi.org/10.1145/3240323.3240347

  24. Valcarce, D., Parapar, J., Barreiro, Á.: Language models for collaborative filtering neighbourhoods. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 614–625. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30671-1_45

    Chapter  Google Scholar 

  25. Valcarce, D., Parapar, J., Barreiro, A.: LiMe: linear methods for pseudo-relevance feedback. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018, pp. 678–687. ACM (2018). https://doi.org/10.1145/3167132.3167207

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Acknowledgments

This work has received support from accreditation 2016–2019 ED431G/01 (Xunta de Galicia/ERDF) and grant FPU17/03210 (MICIU).

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Authors and Affiliations

  1. Information Retrieval Lab, Department of Computer Science, University of A Coruña, A Coruña, Spain

    Alfonso Landin

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  1. Alfonso Landin
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Correspondence to Alfonso Landin .

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Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Landin, A. (2019). Learning User and Item Representations for Recommender Systems. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_45

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  • DOI: https://doi.org/10.1007/978-3-030-15719-7_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15718-0

  • Online ISBN: 978-3-030-15719-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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