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A Self-Adaptive Context-Aware Group Recommender System

  • Reza KhoshkanginiEmail author
  • Maria Silvia Pini
  • Francesca Rossi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10037)

Abstract

The importance role of contextual information on users’ daily decisions led to develop the new generation of recommender systems called Context-Aware Recommender Systems (CARSs). Dependency of users preferences on the context of entities (e.g., restaurant, road, weather) in a dynamic domain, make the recommendation arduous to properly meet the users preferences and gain high level of users’ satisfaction degree, especially in a group recommendation, in which several users need to take a joint decision. In these scenarios may also happen that some users have more weight/importance in the decision process. We propose a self-adaptive CARS (SaCARS) that provides fair services to a group of users who have different importance levels within their group Such services are recommended based on the conditional and qualitative preferences of the users that may change over time based on the different importance levels of the users in the group, on the context of the users, and the context of all the associated entities (e.g., restaurant, weather, other users) in the problem domain. In our framework we model users’ preferences via conditional preference networks (CP-nets) and Time, we adapt Hyperspace Analogue to Context (HAC) model to handle the multi-dimensional context into the system, and sequential voting rule is used to aggregate users’ preferences. We also evaluate the approach experimentally on a real-word scenario. Results show that it is promising.

Keywords

Context-Aware Recommender System CP-net User preferences 

References

  1. 1.
    Khoshkangini, R., Pini, M.S., Rossi, F.: A design of context-aware framework for conditional preferences of group of users. In: Lee, R. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SCI, vol. 653, pp. 97–112. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-33810-1_8 CrossRefGoogle Scholar
  2. 2.
    De Gemmis, M., Iaquinta, L., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Preference learning in recommender systems. Prefer. Learn. 41 (2009)Google Scholar
  3. 3.
    Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)CrossRefGoogle Scholar
  4. 4.
    Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  5. 5.
    Ono, C., Kurokawa, M., Motomura, Y., Asoh, H.: A context-aware movie preference model using a Bayesian network for recommendation and promotion. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 247–257. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73078-1_28 CrossRefGoogle Scholar
  6. 6.
    Setten, M., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application COMPASS. In: Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 235–244. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-27780-4_27 CrossRefGoogle Scholar
  7. 7.
    Rasch, K., Li, F., Sehic, S., Ayani, R., Dustdar, S.: Context-driven personalized service discovery in pervasive environments. World Wide Web 14, 295–319 (2011)CrossRefGoogle Scholar
  8. 8.
    Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: Cp-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. (JAIR) 21, 135–191 (2004)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Lichman, M.: UCI machine learning repository (2013)Google Scholar
  10. 10.
    Smaaberg, S.F., Shabib, N., Krogstie, J.: A user-study on context-aware group recommendation for concerts. In: HT (Doctoral Consortium/Late-breaking Results/Workshops) (2014)Google Scholar
  11. 11.
    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
  12. 12.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. (TOIS) 23, 103–145 (2005)CrossRefGoogle Scholar
  13. 13.
    Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: Workshop on Context-Aware Recommender Systems (CARS 2009) (2009)Google Scholar
  14. 14.
    Oku, K., et al.: A recommendation system considering users past/current/future contexts. In: Proceedings of CARS (2010)Google Scholar
  15. 15.
    Liu, W., Wu, C., Feng, B., Liu, J.: Conditional preference in recommender systems. Expert Syst. Appl. 42, 774–788 (2015)CrossRefGoogle Scholar
  16. 16.
    Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behav. Res. Meth. Instrum. Comput. 1996, 203–208 (1996)CrossRefGoogle Scholar
  17. 17.
    Lang, J.: Graphical representation of ordinal preferences: languages and applications. In: Croitoru, M., Ferré, S., Lukose, D. (eds.) ICCS-ConceptStruct 2010. LNCS (LNAI), vol. 6208, pp. 3–9. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-14197-3_3 CrossRefGoogle Scholar
  18. 18.
    Lang, J., Xia, L.: Sequential composition of voting rules in multi-issue domains. Math. Soc. Sci. 57, 304–324 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Rossi, F., Venable, K.B., Walsh, T.: mCP nets: representing and reasoning with preferences of multiple agents. AAAI 4, 729–734 (2004)Google Scholar
  20. 20.
    Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L.A.: How hard is bribery in elections? JAIR 35, 485–532 (2009)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Maudet, N., Pini, M.S., Venable, K.B., Rossi, F.: Influence and aggregation of preferences over combinatorial domains. In: Proceedings of AAMAS 2012, pp. 1313–1314 (2012)Google Scholar
  22. 22.
    Maran, A., Maudet, N., Pini, M.S., Rossi, F., Venable, K.B.: A framework for aggregating influenced CP-nets and its resistance to bribery. In: Proceedings of AAAI 2013 (2013)Google Scholar
  23. 23.
    Mattei, N., Pini, M.S., Venable, K.B., Rossi, F.: Bribery in voting with CP-nets. Ann. Math. Artif. Intell. 68, 135 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Mattei, N., Pini, M.S., Venable, K.B., Rossi, F.: Bribery in voting over combinatorial domains is easy. In: Proceedings of AAMAS 2012, pp. 1407–1408 (2012)Google Scholar
  25. 25.
    Dalla Pozza, G., Pini, M.S., Rossi, F., Venable, K.B.: Multi-agent soft constraint aggregation via sequential voting. In: Proceedings of IJCAI, pp. 172–177 (2011)Google Scholar
  26. 26.
    Pini, M.S., Rossi, F., Venable, K.B.: Resistance to bribery when aggregating soft constraints. In: Proceedings of AAMAS 2013, pp. 1301–1302 (2013)Google Scholar
  27. 27.
    Pini, M.S., Rossi, F., Venable, K.B.: Bribery in voting with soft constraints. In: Proceedings of AAAI 2013 (2013)Google Scholar
  28. 28.
    Rasch, K., Li, F., Sehic, S., Ayani, R., Dustdar, S.: Automatic description of context-altering services through observational learning. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds.) Pervasive 2012. LNCS, vol. 7319, pp. 461–477. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31205-2_28 CrossRefGoogle Scholar
  29. 29.
    Zhang, H., Berg, A.C., Maire, M., Malik, J.: SVM-KNN: discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006). vol. 2, pp. 2126–2136. IEEE (2006)Google Scholar
  30. 30.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32. ACM (2005)Google Scholar
  31. 31.
    Qian, G., Sural, S., Gu, Y., Pramanik, S.: Similarity between euclidean and cosine angle distance for nearest neighbor queries. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 1232–1237. ACM (2004)Google Scholar
  32. 32.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, New York (2011). doi: 10.1007/978-0-387-85820-3_7 CrossRefGoogle Scholar
  33. 33.
    Allen, T.E., Goldsmith, J., Justice, H.E., Mattei, N., Raines, K.: Generating CP-nets uniformly at random. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI) (2016)Google Scholar
  34. 34.
    Jøsang, A., Guo, G., Pini, M.S., Santini, F., Xu, Y.: Combining recommender and reputation systems to produce better online advice. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds.) MDAI 2013. LNCS (LNAI), vol. 8234, pp. 126–138. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41550-0_12 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Reza Khoshkangini
    • 1
    Email author
  • Maria Silvia Pini
    • 2
  • Francesca Rossi
    • 3
  1. 1.Dep. of MathematicsUniversity of PadovaPadovaItaly
  2. 2.Dep. of Information EngineeringUniversity of PadovaPadovaItaly
  3. 3.IBM T.J. Watson Research CenterYorktown HeightsUSA

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