Context-Aware Group Recommendation Systems
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For a group of friends going to a concert or a festival, finding concerts that everyone is happy with can be challenging as everyone have their own preferences and wishes when it comes to music. In this thesis, a prototype of a group recommendation system for concerts is presented to solve this issue. The prototype is context sensitive; it takes a 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 of these two.The thesis was written following the Design Science Research paradigm. The thesis covers the design and implementation of the prototype in addition to a brief review of the state of the art of the recommendation systems literature. 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 k-Nearest Neighbor and Hybrid approach produces satisfactory results whereas the Matrix Factorization implementation is lagging a bit behind. The users testing the prototype were generally satisfied with the quality of recommendations, however further evaluation is needed to draw any final conclusions.