Relevant context in a movie recommender system: Users' opinion vs. statistical detection
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Context-aware recommender systems help users find their desired content in a reasonable time, by exploiting the pieces of information that describe the situation in which users will consume the items. One of the remaining issues in such systems is determining which contextual information is relevant and which is not. This is an issue since the irrelevant con- textual information can degrade the recommendation quality and it is simply unnecessary to spend resources on the acquisition of the irrelevant data. In this article we compare two approaches: the relevancy assessment from the user survey and the relevancy detection with statistical testing on the rating data. With these approaches we want to see if it is possible for users to predict which context inuences their decisions and which approach leads to better detection of the relevant contextual information.