A User-Study on Context-aware Group Recommendation for Concerts

In this paper, we present a prototype of a group recommendation system for concerts. The prototype is context sensitive taking the user’s location and time into account when giving recommendations. The prototype implements three algorithms to recommend concerts by taking advantage of what users have listened to before: a collaborative filtering algorithm (K-Nearest Neighbor), a Matrix Factorization algorithm, and a Hybrid approach combining these two. The usability of the prototype was evaluated using the System Usability Scale and a user centered evaluation was performed to evaluate the quality of recommendations. The results from the usability evaluation shows that users generally were satisfied with the usability of the prototype. The results from the Quality Evaluation shows that the KNearest Neighbor and Hybrid approach produces satisfactory results whereas the Matrix Factorization implementation was experienced to be a bit poorer. The users testing the prototype were generally satisfied with the quality of recommendations.