Similarity-Based Context-Aware Recommendation

  • Yong Zheng
  • Bamshad Mobasher
  • Robin Burke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9418)


Context-aware recommender systems (CARS) take context into consideration when modeling user preferences. There are two general ways to integrate context with recommendation: contextual filtering and contextual modeling. Currently, the most effective context-aware recommendation algorithms are based on a contextual modeling approach that estimate deviations in ratings across different contexts. In this paper, we propose context similarity as an alternative contextual modeling approach and examine different ways to represent context similarity and incorporate it into recommendation. More specifically, we show how context similarity can be integrated into the sparse linear method and matrix factorization algorithms. Our experimental results demonstrate that learning context similarity is a more effective approach to context-aware recommendation than modeling contextual rating deviations.


Recommender system Context Context-aware Matrix factorization 


  1. 1.
    Abowd, G.D., Dey, A.K.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Mag. 32(3), 67–80 (2011)Google Scholar
  3. 3.
    Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Lüke, K.-H., Schwaiger, R.: 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) CrossRefGoogle Scholar
  4. 4.
    Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context-aware places of interest recommendations for mobile users. In: Marcus, A. (ed.) HCII 2011 and DUXU 2011, Part I. LNCS, vol. 6769, pp. 531–540. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  5. 5.
    Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 301–304. ACM (2011)Google Scholar
  6. 6.
    Baltrunas, L., Ricci, F.: Experimental evaluation of context-dependent collaborative filtering using item splitting. User Model. User-Adap. Inter. 24(1–2), 7–34 (2014)CrossRefGoogle Scholar
  7. 7.
    Chen, A.: Context-aware collaborative filtering system: predicting the user’s preference in the ubiquitous computing environment. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 244–253. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  8. 8.
    Codina, V., Ricci, F., Ceccaroni, L.: Local context modeling with semantic pre-filtering. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 363–366. ACM (2013)Google Scholar
  9. 9.
    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 (2010)Google Scholar
  10. 10.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)CrossRefGoogle Scholar
  11. 11.
    Liu, L., Lecue, F., Mehandjiev, N., Xu, L.: Using context similarity for service recommendation. In: 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), pp. 277–284. IEEE (2010)Google Scholar
  12. 12.
    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. IEEE (2011)Google Scholar
  13. 13.
    Ramirez-Garcia, X., Garca-Valdez, M.: Post-filtering for a restaurant context-aware recommender system. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds.) Recent Advances on Hybrid Approaches for Designing Intelligent Systems, vol. 547, pp. 695–707. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  14. 14.
    Zheng, Y.: Deviation-based and similarity-based contextual SLIM recommendation algorithms. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 437–440. ACM (2014)Google Scholar
  15. 15.
    Zheng, Y.: Improve general contextual SLIM recommendation algorithms by factorizing contexts. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 929–930. ACM (2015)Google Scholar
  16. 16.
    Zheng, Y.: A revisit to the identification of contexts in recommender systems. In: Proceedings of the 20th ACM Conference on Intelligent User Interfaces Companion, pp. 133–136. ACM (2015)Google Scholar
  17. 17.
    Zheng, Y., Burke, R., Mobasher, B.: Differential context relaxation for context-aware travel recommendation. In: Huemer, C., Lops, P. (eds.) EC-Web 2012. LNBIP, vol. 123, pp. 88–99. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  18. 18.
    Zheng, Y., Burke, R., Mobasher, B.: Recommendation with differential context weighting. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 152–164. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  19. 19.
    Zheng, Y., Burke, R., Mobasher, B.: The role of emotions in context-aware recommendation. In: ACM RecSys 2013, Proceedings of the 3rd International Workshop on Human Decision Making in Recommender Systems, pp. 21–28. ACM (2013)Google Scholar
  20. 20.
    Zheng, Y., Burke, R., Mobasher, B.: Splitting approaches for context-aware recommendation: an empirical study. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 274–279. ACM (2014)Google Scholar
  21. 21.
    Zheng, Y., Mobasher, B., Burke, R.: Context recommendation using multi-label classification. In: Proceedings of the 13th IEEE/WIC/ACM International Conference on Web Intelligence, pp. 288–295. IEEE/WIC/ACM (2014)Google Scholar
  22. 22.
    Zheng, Y., Mobasher, B., Burke, R.: CSLIM: contextual SLIM recommendation algorithms. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 301–304. ACM (2014)Google Scholar
  23. 23.
    Zheng, Y., Mobasher, B., Burke, R.: Deviation-based contextual SLIM recommenders. In: Proceedings of the 23rd ACM Conference on Information and Knowledge Management, pp. 271–280. ACM (2014)Google Scholar
  24. 24.
    Zheng, Y., Mobasher, B., Burke, R.: Integrating context similarity with sparse linear recommendation model. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds.) UMAP 2015. LNCS, vol. 9146, pp. 370–376. Springer, Heidelberg (2015) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Center for Web Intelligence, School of ComputingDePaul UniversityChicagoUSA

Personalised recommendations