Abstract
Context-Aware Recommendation Systems (CARS) extend traditional recommendation systems by adapting their output to users’ specific contextual situations. Rating prediction in CARS has been tackled by researchers attempting to recommend appropriate items to users. However, in rating prediction, three thriving challenges are still to tackle: (i) the weight of each context dimension; (ii) the correlation between context dimensions; and (iii) situation inference. A major shortcoming of the classical methods is that there is no defined way to study dependencies and interactions existing among context dimensions. Context-aware algorithms made a strong assumption that context dimensions weights are the same or initialized with random values. To address these issues, we propose a novel approach for weighting context dimensions, studying the correlation between them to infer the current situation then predict the rating based on the inferred situation. Through detailed experimental evaluation we demonstrate that the proposed approach is helpful to improve the prediction accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: Conference on Recommender Systems, pp. 301–304. ACM, New York (2011)
Cingolani, P., Alcalá-Fdez, J.: jFuzzyLogic: a robust andflexible fuzzy-logic inference system language implementation. In: IEEE International Conference on Fuzzy Systems, Australia, pp. 1–8. IEEE (2012)
Ephzibah, E.P.: Advances in computing and information technology. In: First International Conference, India (2011)
Grabisch, M., Kojadinovic, I., Meyer, P.: A review of methods for capacity identification in Choquet integral based multi-attribute utility theory. Eur. J. Oper. Res. 186, 766–785 (2008)
Grabisch, M., Labreuche, C.: A decade of application of the choquet and sugeno integrals in multi-criteria decision aid. Ann. Oper. Res. 175, 247–286 (2009)
Gs, T., Kulkarni, U.P.: Design and implementation of user context aware recommendation engine for mobile using Bayesian network, fuzzy logic and rule base. Int. J. Comput. Appl. U.K. 40, 47–63 (2012)
Liu, X., Aberer, K.: SoCo: a social network aided context-awarerecommender system. In: International Conference on World Wide Web, pp. 781–802. ACM, New York (2013)
Mehta, S.J, Javia, J.: Threshold based knn for fast and more accurate recommendations. In: 2nd IEEE International Conference on Recent Trends in Information Systems, India, pp. 109–113. IEEE (2015)
Ono, C., Takishima, Y., Motomura, Y., Asoh, H.: Context-aware preference model based on a study of difference between real and supposed situation data. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 102–113. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02247-0_12
Sen, A., Larson, M.: From sensors to songs: a learning-free novelmusic recommendation system using contextual sensor data. In: Location-Aware Recommendations, co-located with the Conference on Recommender Systems, Austria, pp. 40–43. CEUR-WS.org (2015)
Zammali, S., Arour, K. Bouzeghoub, A.: A context features selecting and weighting methods for context-aware recommendation. In: 39th IEEE Computer Software and Applications Conference, Italy, pp. 575–584. IEEE Computer Society (2015)
Zheng, Y., Burke, R., Mobasher, B.: Recommendation with differential context weighting, user modeling, adaptation, and personalization. In: 21th International Conference, Italy (2013)
Zheng, Y., Burke, R., Mobasher, B.: The role of emotions in context-aware recommendation. In: International Workshop on Human Decision Making in Recommender Systems, China, pp. 21–28. CEUR-WS.org (2013)
Zheng, Y., Mobasher, B., Burke, R.: Incorporating context correlation into context-aware matrix factorization. In: Proceedings of International Conference on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization, Germany, pp. 21–27. CEUR-WS.org (2015)
Zheng, Y., Mobasher, B., Burke, R.D.: CARSKit: A Java-based context-aware recommendation engine. In: IEEE International Conference on Data Mining Workshop, Atlantic City, NJ, USA, 14–17 November 2015, pp. 1668–1671. IEEE Computer Society, Los Alamitos (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Dridi, R., Zammali, S., Arour, K. (2016). Situation-Aware Rating Prediction Using Fuzzy Rules. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-47650-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47649-0
Online ISBN: 978-3-319-47650-6
eBook Packages: Computer ScienceComputer Science (R0)