User Profiling in Text-Based Recommender Systems Based on Distributed Word Representations

  • Anton Alekseev
  • Sergey Nikolenko
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)


We introduce a novel approach to constructing user profiles for recommender systems based on full-text items such as posts in a social network and implicit ratings (in the form of likes) that users give them. The profiles measure a user’s interest in various topics mined from the full texts of the items. As a result, we get a user profile that can be used for cold start recommendations for items, targeted advertisement, and other purposes. Our experiments show that the method performs on a level comparable with classical collaborative filtering algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended.


User profiling Recommender systems Distributed word representations 



This work was supported by the “Recommendation Systems with Automated User Profiling” project sponsored by Samsung and the Government of the Russian Federation grant 14.Z50.31.0030. We thank Dmitry Bugaichenko and the “Odnoklassniki” social network for providing us with the social network dataset with texts of posts and user likes and Alexander Panchenko and Nikolay Arefyev for the trained word2vec model along with its Russian-language training data.


  1. 1.
    Webb, G.I., Pazzani, M.J., Billsus, D.: Machine learning for user modeling. User Model. User-Adap. Inter. 11(1–2), 19–29 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Johnson, A., Taatgen, N.: User modeling. In: Handbook of Human Factors in Web Design. Lawrence Erlbaum, pp. 424–439 (2005)Google Scholar
  3. 3.
    Fischer, G.: User modeling in human–computer interaction. User Model. User-Adap. Inter. 11(1–2), 65–86 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): The Adaptive Web: Methods and Strategies of Web Personalization. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Bjorkoy, O.: User modeling on the web: an exploratory review of recommendation systems. PhD thesis, NTNU Trondheim (2010)Google Scholar
  6. 6.
    Lops, P., Gemmis, M.D., Semeraro, G., Lops, P., Gemmis, M.D., Semeraro, G.: Chapter 3 content-based recommender systems: state of the art and trendsGoogle Scholar
  7. 7.
    Pazzani, M.J., Billsus, D.: The Adaptive Web. Springer, Heidelberg (2007)Google Scholar
  8. 8.
    Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification ofinteresting web sites. Mach. Learn. 27(3), 313–331 (1997)CrossRefGoogle Scholar
  9. 9.
    Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)CrossRefGoogle Scholar
  10. 10.
    Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Model. User-Adap. Inter. 10(2–3), 147–180 (2000)CrossRefGoogle Scholar
  11. 11.
    Cohen, W.W.: Fast effective rule induction. In: 12th International Conference on Machine Learning (ML95), pp. 115–123 (1995)Google Scholar
  12. 12.
    Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the 15th National/10th Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, AAAI 1998/IAAI 1998, pp. 714–720, Menlo Park, CA, USA. AAAI (1998)Google Scholar
  13. 13.
    Al-Rfou, R., Perozzi, B., Skiena, S.: Polyglot: distributed word representations for multilingual NLP. In: Proceedings of the 17th Conference on Computational Natural Language Learning, Sofia, Bulgaria, ACL, pp. 183–192, August 2013Google Scholar
  14. 14.
    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), Doha, Qatar, Association for Computational Linguistics, pp. 1532–1543, October 2014Google Scholar
  15. 15.
    Panchenko, A., Loukachevitch, N., Ustalov, D., Paperno, D., Meyer, C.M., Konstantinova, N.: RUSSE: the first workshop on Russian semantic similarity. In: Proceedings of the International Conference on Computational Linguistics and Intellectual Technologies (Dialogue), pp. 89–105, May 2015Google Scholar
  16. 16.
    Kumar, B.V., Kotsia, I., Patras, I.: Max-margin non-negative matrix factorization. Image Vision Comput. 30(45), 279–291 (2012)CrossRefGoogle Scholar
  17. 17.
    Arefyev, N., Panchenko, A., Lukanin, A., Lesota, O., Romanov, P.: Evaluating three corpus-based semantic similarity systems for Russian. In: Proceedings of International Conference on Computational Linguistics Dialogue (2015, to appear)Google Scholar
  18. 18.
    Vorontsov, K., Frei, O., Apishev, M., Romov, P., Suvorova, M., Yanina, A.: Non-Bayesian additive regularization for multimodal topic modeling of large collections. In: Proceedings of the 2015 Workshop on Topic Models: Post-Processing and Applications, TM 2015, pp. 29–37, New York, NY, USA. ACM (2015)Google Scholar
  19. 19.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)zbMATHGoogle Scholar
  20. 20.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. SIGMOD Rec. 25(2), 103–114 (1996)CrossRefGoogle Scholar
  21. 21.
    Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min. Knowl. Discov. 2(2), 169–194 (1998)CrossRefGoogle Scholar
  22. 22.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  23. 23.
    Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 1177–1178, New York, NY, USA. ACM(2010)Google Scholar
  24. 24.
    Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027–1035, Philadelphia, PA, USA. Society for Industrial and Applied Mathematics (2007)Google Scholar
  25. 25.
    Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)CrossRefGoogle Scholar
  26. 26.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Jebara, T., Xing, E.P., (eds.) Proceedings of the 31st International Conference on Machine Learning, JMLR Workshop and Conference Proceedings, pp. 1188–1196 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Steklov Mathematical Institute at St. PetersburgSt. PetersburgRussia
  2. 2.National Research University Higher School of EconomicsSt. PetersburgRussia
  3. 3.Deloitte Analytics InstituteMoscowRussia

Personalised recommendations