Relevant Context in a Movie Recommender System: Users’ Opinion vs. Statistical Detection

4th Workshop on Context-Aware Recommender Systems; in conjunction with the 6th ACM Conference on Recommender Systems (RecSys 2012)

Ante Odic´ ; Marko Tkalcˇicˇ ; lj.si

Others  :  http://ceur-ws.org/Vol-889/paper2.pdf
PID  :  43410

来源: CEUR

【 摘 要 】

Context-aware recommender systems help users nd 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 sys- tems 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 qual- ity 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 sur- vey 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