A Context Aware Recommendation System for movies
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People like to watch movies, but they do not always know what they want tosee. Although there exist applications that helps people solve this issue, theyare not being used very often (see Appendix A). People tend to do their ownresearch to decide what to wach. This thesis will be looking into this issue andprovide a proof of concept prototype. Not only does the prototype recommendmovies, but it also introduces an social feature that can be extended to so muchmore. This feature were found by doing a round of interviews, while trying tond the cause of why people do not use the recommendation systems.The thesis describes some of the state of the art approaches to recommenda-tion system algorithms and the implementation of the prototype. The prototypewas evaluated by its accuracy and speed using data sets from Movielens.The results show that the chosen approach to recommendation systems isviable, and that the accuracy of the recommendation is not the only reason thatrecommendation systems are not being used that much. The results show thata social feature might increase the use of recommendation systems.