Demo Papers

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

AuthorAlan Said, Ernesto William De Luca, Benjamin Kille, Brijnesh Johannes Jain, Immo Micus, Sahin Albayrak
SourceProceedings of the 17th international conference on Intelligent user interfaces (IUI'12) 
LinksBibTeX   |   Uni-Library 

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.