Integrating context-awareness and multi-criteria decision making in educational learning
Recommender system is a well-known information system which can capture user tastes and produce item recommendations to the end users. Context-aware recommender systems (CARS) additionally take contexts (e.g., location, time, weather, etc) into consideration, and multi-criteria recommender systems (MCRS) utilize user preferences in multiple criteria to better generate recommendations. Both CARS and MCRS have been widely applied in the real-world applications, such as tourism, movies, music and dining. However, there are no existing research which exploits the methods to integrate them together, not to mention the contributions in the area of educational learning. In this paper, we make the first attempt to integrate context-awareness and multi-criteria decision making in the recommender systems by using the educational data as a case study. Our experimental results reveal that it is able to help produce more accurate recommendations by taking advantage of these two recommendation strategies. We also perform experiments on a tourism data set to demonstrate that the proposed methods can also be generalized to other domains.