Exploratory study of multi-criteria recommendation algorithms over technology enhanced learning datasets

Abstract

Results of previous studies have indicated that the performance of recommendation algorithms depends on the characteristics of the application context. The same algorithms have shown to be performing in totally different ways when a new or evolved data set is considered, thus leading to a need for continuous monitoring of how they operate in a realistic setting. In this paper we investigate such a real life implementation of a multi-criteria recommender system and try to identify the needed adjustments that need to take place in order for it to better match the requirements of its operational environment. More specifically, we examine the case of a multi-attribute collaborative filtering algorithm that has been supporting the recommendation service within a Web portal for organic and sustainable education. Our study particularly explores the experimental performance of the already implemented algorithm, as well as an alternative one, using data from the intended application, a simulated expansion of it, and from similar portals. The results of this study indicate the importance of the frequent experimental investigation of a recommender system's various design options, and the need for the exploration of adaptive implementations in real life recommender systems.