Advertisement

Context-Aware Collaborative Prediction

  • Shu WuEmail author
  • Qiang Liu
  • Liang Wang
  • Tieniu Tan
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Context-aware collaborative prediction takes contextual information into consideration when modeling user preferences and predicting user behaviors. There are two general ways to integrate contexts with collaborative prediction: contextual filtering and contextual modeling. Contextual filtering uses contexts to select data and adjust the result, while contextual modeling takes contexts into the model construction. Currently, the most effective context-aware collaborative prediction algorithms are based on the contextual modeling approach, which generates contextual representations or context-aware representations. This chapter reviews some related tasks of collaborative prediction, such as conventional recommendation, sequential recommendation, and multi-domain relation prediction. In addition, it also introduces some recent works on representation learning and methods of specific applications, such as context-aware recommendation, latent collaborative retrieval, tag recommendation, and click-through rate prediction.

References

  1. 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(1), 103–145 (2005)CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253. Springer, Berlin (2011)Google Scholar
  3. 3.
    Agarwal, D., Chen, B.C.: Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 19–28. ACM, New York (2009)Google Scholar
  4. 4.
    Bahadori, M.T., Yu, Q.R., Liu, Y.: Fast multivariate spatio-temporal analysis via low rank tensor learning. In: NIPS, pp. 3491–3499. (2014)Google Scholar
  5. 5.
    Baltrunas, L., Ricci, F.: Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the third ACM conference on Recommender systems, pp. 245–248. ACM, New York (2009)Google Scholar
  6. 6.
    Baroni, M., Zamparelli, R.: Nouns are vectors, adjectives are matrices: representing adjective-noun constructions in semantic space. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1183–1193. Association for Computational Linguistics (2010)Google Scholar
  7. 7.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  8. 8.
    Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  9. 9.
    Chen, J., Wang, C., Wang, J.: A personalized interest-forgetting markov model for recommendations. In: AAAI, pp. 16–22. (2015)Google Scholar
  10. 10.
    Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: IJCAI, pp. 2605–2611. (2013)Google Scholar
  11. 11.
    Ding, Y., Li, X.: Time weight collaborative filtering. In: CIKM, pp. 485–492. (2005)Google Scholar
  12. 12.
    Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new poi recommendation. In: IJCAI, pp. 2069–2075. (2015)Google Scholar
  13. 13.
    Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latenttopic sequential patterns. In: RecSys, pp. 131–138. (2012)Google Scholar
  14. 14.
    Hsiao, K.J., Kulesza, A., Hero, A.: Social collaborative retrieval. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 293–302. ACM, New York (2014)Google Scholar
  15. 15.
    Jamali, M., Lakshmanan, L.: Heteromf: recommendation in heterogeneous information networks using context dependent factor models. In: Proceedings of the 22nd international conference on World Wide Web, pp. 643–654. International World Wide Web Conferences Steering Committee, Geneva (2013)Google Scholar
  16. 16.
    Kapoor, K., Subbian, K., Srivastava, J., Schrater, P.: Just in time recommendations: Modeling the dynamics of boredom in activity streams. In: WSDM, pp. 233–242. (2015)Google Scholar
  17. 17.
    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, New York (2010)Google Scholar
  18. 18.
    Kim, S., Lee, S., Kim, J., Yoon, Y.I.: Mrtensorcube: tensor factorization with data reduction for context-aware recommendations. J. Supercomput. (2017)Google Scholar
  19. 19.
    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, New York (2008)Google Scholar
  20. 20.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  21. 21.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 145–186. Springer, Berlin (2011)Google Scholar
  22. 22.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  23. 23.
    Lathia, N., Hailes, S., Capra, L.: Temporal collaborative filtering with adaptive neighbourhoods. In: SIGIR, pp. 796–797. (2009)Google Scholar
  24. 24.
    Li, Y., Nie, J., Zhang, Y., Wang, B., Yan, B., Weng, F.: Contextual recommendation based on text mining. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 692–700. Association for Computational Linguistics (2010)Google Scholar
  25. 25.
    Lippert, C., Weber, S.H., Huang, Y., Tresp, V., Schubert, M., Kriegel, H.P.: Relation prediction in multi-relational domains using matrix factorization. In: Workshops of Neural Information Processing Systems: Structured Input-Structured Output. (2008)Google Scholar
  26. 26.
    Liu, N.N., Zhao, M., Xiang, E., Yang, Q.: Online evolutionary collaborative filtering. In: RecSys, pp. 95–102. (2010)Google Scholar
  27. 27.
    Liu, Q., Wu, S., Wang, L.: Collaborative prediction for multi-entity interaction with hierarchical representation. In: CIKM, pp. 613–622. (2015)Google Scholar
  28. 28.
    Liu, Q., Wu, S., Wang, L.: Cot: Contextual operating tensor for context-aware recommender systems. In: Proceedings of the 29th International AAAI Conference on Artificial Intelligence, pp. 203–209. AAAI, California (2015)Google Scholar
  29. 29.
    Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: A recurrent model with spatial and temporal contexts. In: AAAI, pp. 194–200. (2016)Google Scholar
  30. 30.
    Liu, X., Aberer, K.: Soco: a social network aided context-aware recommender system. In: Proceedings of the 22nd international conference on World Wide Web, pp. 781–802. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  31. 31.
    Liu, X., Wu, W.: Learning context-aware latent representations for context-aware collaborative filtering. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, pp. 887–890, 9–13 Aug, 2015Google Scholar
  32. 32.
    McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D., et al.: Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1222–1230. ACM, New York (2013)Google Scholar
  33. 33.
    Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: INTERSPEECH, pp. 1045–1048. (2010)Google Scholar
  34. 34.
    Mikolov, T., Kombrink, S., Burget, L., Cernocky, J.H., Khudanpur, S.: Extensions of recurrent neural network language model. In: ICASSP, pp. 5528–5531. (2011)Google Scholar
  35. 35.
    Mikolov, T., Kombrink, S., Deoras, A., Burget, L., Cernocky, J.: Rnnlm-recurrent neural network language modeling toolkit. In: ASRU Workshop, pp. 196–201. (2011)Google Scholar
  36. 36.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings on Neural Information Processing Systems. (2013)Google Scholar
  37. 37.
    Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Proceedings on Neural Information Processing systems. (2007)Google Scholar
  38. 38.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Using sequential and non-sequential patterns in predictive web usage mining tasks. In: ICDM, pp. 669–672. (2002)Google Scholar
  39. 39.
    Natarajan, N., Shin, D., Dhillon, I.S.: Which app will you use next?: Collaborative filtering with interactional context. In: RecSys, pp. 201–208. (2013)Google Scholar
  40. 40.
    Oentaryo, R.J., Lim, E.P., Low, J.W., Lo, D., Finegold, M.: Predicting response in mobile advertising with hierarchical importance-aware factorization machine. In: Proceedings of the 7th ACM international conference on Web search and data mining, pp. 123–132. ACM, New York (2014)Google Scholar
  41. 41.
    Palmisano, C., Tuzhilin, A., Gorgoglione, M.: Using context to improve predictive modeling of customers in personalization applications. IEEE Trans. Knowl. Data Eng. 20(11), 1535–1549 (2008)CrossRefGoogle Scholar
  42. 42.
    Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre-vs. post-filtering approaches in context-aware recommender systems. In: Proceedings of the third ACM conference on Recommender systems, pp. 265–268. ACM, New York (2009)Google Scholar
  43. 43.
    Rendle, S.: Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 57 (2012)Google Scholar
  44. 44.
    Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 727–736. ACM, New York (2009)Google Scholar
  45. 45.
    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, pp. 452–461. AUAI Press (2009)Google Scholar
  46. 46.
    Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: WWW, pp. 811–820. (2010)Google Scholar
  47. 47.
    Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 635–644. ACM, New York (2011)Google Scholar
  48. 48.
    Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 81–90. ACM, New York (2010)Google Scholar
  49. 49.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 5, 3 (1988)zbMATHGoogle Scholar
  50. 50.
    Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A.: Cars2: Learning context-aware representations for context-aware recommendations. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 291–300. ACM, New York (2014)Google Scholar
  51. 51.
    Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., Oliver, N.: Tfmap: Optimizing map for top-n context-aware recommendation. In: SIGIR, pp. 155–164. (2012)Google Scholar
  52. 52.
    Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 650–658. ACM, New York (2008)Google Scholar
  53. 53.
    Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934. (2013)Google Scholar
  54. 54.
    Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1201–1211. Association for Computational Linguistics (2012)Google Scholar
  55. 55.
    Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1631–1642. Association for Computational Linguistics (2013)Google Scholar
  56. 56.
    Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: Large scale online bayesian recommendations. In: Proceedings of the 18th International Conference on World Wide Web, pp. 111–120. International World Wide Web Conferences Steering Committee (2009)Google Scholar
  57. 57.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the Second ACM Conference on Recommender Systems, pp. 43–50. ACM, New York (2008)Google Scholar
  58. 58.
    Tucker, L.R.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279–311 (1966)MathSciNetCrossRefGoogle Scholar
  59. 59.
    Wang, P., Guo, J., Lan, Y., Xu, J., Wan, S., Cheng, X.: Learning hierarchical representation model for next basket recommendation. In: SIGIR, pp. 403–412. ACM, New York (2015)Google Scholar
  60. 60.
    Weston, J., Wang, C., Weiss, R., Berenzweig, A.: Latent collaborative retrieval. In: Proceedings of the 29th International Conference on Machine Learning, pp. 9–16. ACM, New York (2012)Google Scholar
  61. 61.
    Wu, K.K., Liu, P., Meng, H.M., Yam, Y.: An embedding approach for context-aware collaborative recommendation and visualization. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, pp. 3457–3462, 9–12 Oct, 2016.  https://doi.org/10.1109/SMC.2016.7844768
  62. 62.
    Wu, S., Liu, Q., Wang, L., Tan, T.: Contextual operation for recommender systems. IEEE TKDE 28, 2000–2012 (2016)Google Scholar
  63. 63.
    Xiong, L., Chen, X., Huang, T.K., Schneider, J.G., Carbonell, J.G.: Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 211–222. SIAM, Philadelphia (2010)Google Scholar
  64. 64.
    Yan, L., Li, W.J., Xue, G.R., Han, D.: Coupled group lasso for web-scale ctr prediction in display advertising. In: Proceedings of the 31th International Conference on Machine Learning, pp. 802–810. ACM, New York (2014)Google Scholar
  65. 65.
    Yang, Q., Fan, J., Wang, J., Zhou, L.: Personalizing web page recommendation via collaborative filtering and topic-aware markov model. In: ICDM, pp. 1145–1150. (2010)Google Scholar
  66. 66.
    Yang, S.H., Long, B., Smola, A., Sadagopan, N., Zheng, Z., Zha, H.: Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the 20th international conference on World Wide Web, pp. 537–546. International World Wide Web Conferences Steering Committee (2011)Google Scholar
  67. 67.
    Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: SIGIR, pp. 729–732. (2016)Google Scholar
  68. 68.
    Zhang, L., Agarwal, D., Chen, B.C.: Generalizing matrix factorization through flexible regression priors. In: Proceedings of the fifth ACM conference on Recommender systems, pp. 13–20. ACM, New York (2011)Google Scholar
  69. 69.
    Zhang, Y., Dai, H., Xu, C., Feng, J., Wang, T., Bian, J., Wang, B., Liu, T.Y.: Sequential click prediction for sponsored search with recurrent neural networks. In: AAAI, pp. 1369–1376. (2014)Google Scholar
  70. 70.
    Zheng, Y., Burke, R.D., Mobasher, B.: Splitting approaches for context-aware recommendation: an empirical study. In: Symposium on Applied Computing, SAC 2014, Gyeongju, Republic of Korea, pp. 274–279, 24–28 March, 2014Google Scholar
  71. 71.
    Zhong, E., Fan, W., Yang, Q.: Contextual collaborative filtering via hierarchical matrix factorization. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 744–755. SIAM, Philadelphia (2012)Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations