Review Paper on Efficient Approach for Context Aware Recommendation System
Keywords:
Context-Aware Recommender Systems(CARs); Cold-Start Problem; Hybrid SystemAbstract
Context-Aware Recommender Systems (CARSs) have to face the cold-start problem, that is, there is no possibility to provide proper recommendations for the new users, items or contextual situations. In this paper, the methods proposed for solving cold start problem by exploiting various hybridization techniques, in order to take advantage of the strengths of different CARS algorithms while removing their weaknesses in a given (cold start) situation. The initial analysis has shown that basic CARS algorithms are used and hybridized to achieve an overall optimal performance. Here, combined multiple pre-filters (Combining Multiple approaches) are used with Hybridization to solve the cold-start problem. It is used to generate accurate ratings and better performance of CARS.
References
- Hybridization Techniques Using combined Multiple Approaches for Cold-Start Problem in Context Aware Recommendation System” International Journal of Innovation & Advancement in Computer Science IJIACS ISSN 2347-8616 Volume 6, Issue 5 May 2017.
- Jing Liu, Yu Jiang, Zechao Li, Xi Zhang. “DomainSensitive Recommendation with User-Item Subgroup Analysis” In: IEEE Transaction on Knowledge and Data Engineering, Vol. 28, No. 4, April 2016.
- Shu Wu, Qiang Liu. “Contextual Operation for Recommender Systems” In: IEEE Transaction on Knowledge and Data Engineering, Volume: 28, Issue: 8, Aug. 1 2016. DOI: 10.1109/TKDE.2016.2562621.
- Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., & Pedone, A. Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In RecSys'09: Proceedings of the third ACM conference on Recommender systems, New York, New York, USA, 2009 (pp. 265-268): ACM.
- Mettouris C., Papadopoulos G. A. (2013) Contextual Modelling in Context-Aware Recommender Systems: A Generic Approach. In: Haller A., Huang G., Huang Z., Paik H., Sheng Q.Z. (eds) Web Information System Engineering, Volume 7652. Springer.
- Jing Liu, Yu Jiang, Zechao Li, Xi Zhang. “DomainSensitive Recommendation with User-Item Subgroup Analysis” In: IEEE Transaction on Knowledge and Data Engineering, Vol. 28, No. 4, April 2016.
- Pedro G. Campos, Ignacio Fernandez-Tobias, Ivan Cantador, Fernando Díez “Context-Aware Movie Recommendations: An Empirical Comparison of Pre-Filtering, Post-Filtering and Contextual Modelling Approaches” In: Lecture Notes in Business Information Processing 152:137-149, August 2013.
- Victor Codina, Francesco Ricci, Luigi Ceccaroni. “Semantically-Enhanced Pre-Filtering for Context Aware Recommender Systems” In: 3rd Workshop on Context-awareness in Retrieval and Recommendation. DOI: 10.1145/2442670.2442674,ACM 2013.
- U. Panniello, A. Tuzhilin, M. Gorgoglione, C. Palmisano, A. Pedone. Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In RecSys’09, pages 265-268, 2009.
- Adomavicius, G., Sankaranarayanan, R.,Sen, S., and Tuzhilin, A. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM T. Inform. Syst. 23, 1 (2005), 103-145.
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