Vectors of Pairwise Item Preferences

  • Gaurav PandeyEmail author
  • Shuaiqiang Wang
  • Zhaochun Ren
  • Yi Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)


Neural embedding has been widely applied as an effective category of vectorization methods in real-world recommender systems. However, its exploration of users’ explicit feedback on items, to create good quality user and item vectors is still limited. Existing neural embedding methods only consider the items that are accessed by the users, but neglect the scenario when a user gives high or low rating to a particular item. In this paper, we propose Pref2Vec, a method to generate vector representations of pairwise item preferences, users and items, which can be directly utilized for machine learning tasks. Specifically, Pref2Vec considers users’ pairwise item preferences as elementary units. It vectorizes users’ pairwise preferences by maximizing the likelihood estimation of the conditional probability of each pairwise item preference given another one. With the pairwise preference matrix and the generated preference vectors, the vectors of users are yielded by minimizing the difference between users’ observed preferences and the product of the user and preference vectors. Similarly, the vectorization of items can be achieved with the user-item rating matrix and the users vectors. We conducted extensive experiments on three benchmark datasets to assess the quality of item vectors and the initialization independence of the user and item vectors. The utility of our vectorization results is shown by the recommendation performance achieved using them. Our experimental results show significant improvement over state-of-the-art baselines.


Vectorization Neural embedding Recommender systems 


  1. 1.
    Barkan, O., Koenigstein, N.: Item2Vec: neural item embedding for collaborative filtering. In: MLSP, pp. 1–6 (2016)Google Scholar
  2. 2.
    Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  3. 3.
    Bhingardive, S., Singh, D., V, R., Redkar, H.H., Bhattacharyya, P.: Unsupervised most frequent sense detection using word embeddings. In: HLT-NAACL, pp. 1238–1243 (2015)Google Scholar
  4. 4.
    Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)Google Scholar
  5. 5.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)Google Scholar
  6. 6.
    Choi, S., Cha, S., Tapper, C.C.: A survey of binary similarity and distance measures. J. Systemics Cybern. Inform. 8(1), 43–48 (2010)Google Scholar
  7. 7.
    Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. J. Art. Int. Res. 10(1), 243–270 (1999)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)zbMATHGoogle Scholar
  9. 9.
    Djuric, N., Wu, H., Radosavljevic, V., Grbovic, M., Bhamidipati, N.: Hierarchical neural language models for joint representation of streaming documents and their content. In: WWW, pp. 248–255 (2015)Google Scholar
  10. 10.
    Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1990)zbMATHGoogle Scholar
  11. 11.
    Grbovic, M., et al.: E-commerce in your inbox: Product recommendations at scale. In: SIGKDD, pp. 1809–1818 (2015)Google Scholar
  12. 12.
    Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: SIGKDD, pp. 855–864 (2016)Google Scholar
  13. 13.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)Google Scholar
  14. 14.
    He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558 (2016)Google Scholar
  15. 15.
    Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. ACM Trans. Inf. Syst. 5, 287–310 (2002)Google Scholar
  16. 16.
    Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. Wiley, Hoboken (2013)CrossRefGoogle Scholar
  17. 17.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)CrossRefGoogle Scholar
  18. 18.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  19. 19.
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: HLT-NAACL, pp. 260–270 (2016)Google Scholar
  20. 20.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)Google Scholar
  21. 21.
    Liu, N.N., Yang, Q.: Eigenrank: a ranking-oriented approach to collaborative filtering. In: SIGIR, pp. 83–90 (2008)Google Scholar
  22. 22.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)Google Scholar
  23. 23.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)Google Scholar
  24. 24.
    Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: SIGKDD, pp. 701–710 (2014)Google Scholar
  25. 25.
    Schwenk, H.: Continuous space language models. Comput. Speech Lang. 21(3), 492–518 (2007)CrossRefGoogle Scholar
  26. 26.
    Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: NIPS, pp. 926–934 (2013)Google Scholar
  27. 27.
    Socher, R., Lin, C.C., Ng, A.Y., Manning, C.D.: Parsing natural scenes and natural language with recursive neural networks. In: ICML (2011)Google Scholar
  28. 28.
    Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: ACL, pp. 384–394 (2010)Google Scholar
  29. 29.
    Turney, P.D.: Distributional semantics beyond words: supervised learning of analogy and paraphrase. TACL 1, 353–366 (2013)Google Scholar
  30. 30.
    Zou, W.Y., Socher, R., Cer, D.M., Manning, C.D.: Bilingual word embeddings for phrase-based machine translation. In: EMNLP, pp. 1393–1398 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gaurav Pandey
    • 1
    Email author
  • Shuaiqiang Wang
    • 2
  • Zhaochun Ren
    • 2
  • Yi Chang
    • 3
  1. 1.University of JyvaskylaJyväskyläFinland
  2. 2.JD.comBeijingChina
  3. 3.Jilin UniversityChangchunChina

Personalised recommendations