Alan Said
Ernesto William De Luca
Benjamin Kille
ABSTRACT
Collaborative Filtering Recommender Systems come in a wide variety of variants. In this paper we present a system for visualizing and comparing recommendations provided by different collaborative recommendation algorithms. The system utilizes a set of context-aware, hybrid, and other collaborative filtering solutions in order to generate various recommendations from which its users can pick those corresponding best to their current situation (i.e. context). All user interaction is fed back to the system in order to additionally improve the quality of the recommendations. Additionally, users can explicitly ask the system to treat certain recommenders as more important than others, or disregard them completely if the list of recommended movies is not to their liking.
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Digital Library
- Needham, C. IMDb history. http: //www.imdb.com/help/show_leaf?history 12/2009.Google Scholar
- Said, A., Berkovsky, S., De Luca, E. W., and Hermanns, J. Challenge on context-aware movie recommendation: CAMRa2011. In Proc. of the 5th ACM Conf. on Recommender systems, RecSys '11, ACM (2011). Google ScholarDigital Library
- Said, A., De Luca, E. W., and Albayrak, S. Inferring contextual user profiles - improving recommender performance. In Proc. of the 3rd Workshop on Context-Aware Recommender Systems, CARS '11 (2011).Google Scholar
- Said, A., De Luca, E. W., and Albayrak, S. Using social and pseudo social networks to improve recommendation. In Proc. of the 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP'11) (2011).Google Scholar
- Said, A., Jain, B., and Albayrak, S. Analyzing weighting schemes in collaborative filtering: Cold start, post cold start and power users. In Proc. of the 2012 ACM symposium on Applied computing, SAC '12, ACM (2012). Google ScholarDigital Library
- Said, A., Kille, B., De Luca, E. W., and Albayrak, S. Personalizing tags: a folksonomy-like approach for recommending movies. In Proc. of the 2nd Intl. Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec '11, ACM (2011). Google ScholarDigital Library
- Said, A., Plumbaum, T., De Luca, E. W., and Albayrak, S. A comparison of how demographic data affects recommendation. In Adjoint Proc. of the 19th international conference on User modeling, adaption, and personalization (2011).Google Scholar
Index Terms
- KMulE: a framework for user-based comparison of recommender algorithms
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