Understanding and Using Context
Personal and Ubiquitous Computing
Personalizing tags: a folksonomy-like approach for recommending movies
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Challenge on context-aware movie recommendation: CAMRa2011
Proceedings of the fifth ACM conference on Recommender systems
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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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.