KMulE: A Framework for User-based Comparison ofRecommender Algorithms                                                   Al...
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KMulE: A Framework for User-based Comparison of Recommender Algorithms


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Demo presented at IUI 2012.
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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KMulE: A Framework for User-based Comparison of Recommender Algorithms

  1. 1. KMulE: A Framework for User-based Comparison ofRecommender Algorithms Alan Said, Ernesto W. De Luca, Benjamin Kille, Brijnesh J. Jain, Immo Micus, Sahin AlbayrakKMulE MotivationKMulE is a framework for user-based comparison of Recommender systems aim at presenting the most suitable itemsmovie recommendations. Recommendations are for their users. A system will usually present one set ofgenerated by a set of algorithms allowing users to pick recommendations for their users, not taking into consideration the context of the user. KMulE presents several lists ofwhich one fits their current mood best. Each set of recommendations, allowing users to select whichrecommendations is presented with a short description recommendations fit their current situation best. Collecting thison how it works, e.g. whether it is based on the users, information will allow for more accurate context-awareage, location, movie popularity, etc. recommendations in the future.The KMulE User Interface Interface Description Each column in the user interface shows recommendations from one recommendation algorithm allowing users to discover movies based on user-based, movie-based features or system-wide features . Available algorithms ● Pearson Similarity-based k-NN (baseline) ● Age-based k-NN ● Gender-based k-NN ● Inverse popularity-based k-NN ● Location-based k-NN ● Rating distribution similarity-based k-NN ● Easily extendable to include moreBibliography The recommender backend• Said, A., De Luca, E. W., and Albayrak, S. Inferring contextual KMulE is implemented on top of an extended version of Apache user profiles - improving recommender performance. CARS ’11 Mahout’s Taste library using the Movielens 1 million dataset. (2011). The demo is running on a Core i5 laptop, memory usage per• Said, A., De Luca, E. W., and Albayrak, S. Using social and algorithm is 200-800mb. pseudo social networks to improve recommendation. ITWP’11 (2011). The whole system will be released as open source during 2012.• Said, A., Jain, B., and Albayrak, S. Analyzing weighting schemes in collaborative filtering: Cold start, post cold start and power users. ACM SAC ’12 (2012). Acknowledgments• Said, A., Kille, B., De Luca, E. W., and Albayrak, S. Personalizing The work in this paper was conducted tags: a folksonomy-like approach for recommending movies. in the scope of the KMulE project which HetRec ’11 (2011). was sponsored by the German Federal• Said, A., Plumbaum, T., De Luca, E. W., and Albayrak, S. A Ministry of Economics and Technology comparison of how demographic data affects (BMWi). recommendation. UMAP’11 (2011). The authors would like to express their• Said, A., Kille, B., Jain, B.J., Albayrak, S. Increasing Diversity gratitude to the moviepilot team for Through Furthest Neighbor-Based Recommendation. DDR’12 their relevant insights and support. (2012) DAI Lab, Technische Universität Berlin (