ars. Collaborative filtering (CF) identifies similar users and provides recommendations based upon their usage patterns. CF has been extensively employed in RecSysTEL; an early LAK paper, Verbert2011 evaluated and compared the performance of different CF techniques on educational data sets, showing that the best choice of algorithm is data dependent. In a more recent study, Kopeinik2017 also concluded that the performance of the algorithms strongly depends on the properties and characteristics of the particular dataset. In combining educational data sets with social networks, Cechinel2013 used CF to predict the utility of items for users based on their interest and the interest of the network of the users around them. Similarly, Fazeli2014 proposed a graph-based approach that uses graph-walking for improving performance on educational data sets.
One important way in which RecSysTEL has been used in an educational setting is to recommend personalized learning objects. Thus, Lemire2005 used inference rules to provide context aware recommendation on learning objects, and Mangina2008 recommend documents and resources within e-learning environments to expand or reinforce knowledge. Interestingly, Gomez-Albarran2009 combined content based filtering with collaborative filtering to make recommendations in a student authored repository. When recommending learning objects (e.g. questions) related to student, Cazella2010 provided a semi-automated, hybrid solution based on CF (nearest neighbor) and rule based filtering, while Thai-Nghe2011 used students’ performance predictions to recommend more appropriate exercises. CF techniques (basic, biased, and tensor matrix factorization) were used to address a number of different student behaviors and to model the temporal effect of students improving over time. Recently, Imran2016 provided an automated solution to personalize a learning management systems (LMS) using advanced learners’ profiles to encapsulate their expertise level, prior knowledge, and performance in the course. The approach used association rule mining to create the learning object recommendations.
Matrix factorization (MF) is one of the most established techniques used in CF; however, despite its success in RecSys, MF has rarely been used in RecSysTEL. Out of the 124 papers referenced by Drachsler2015, only two papers directly use it [Salehi (2013), Thai-Nghe et al. (2011)]. This is somewhat surprising; MF has been put to good use in EDM for generating latent profiles of student expertise and so ought to combine with RecSysTEL in a straightforward manner. Indeed, the intelligent tutoring systems that appear to be utilized in that body of work could be seen as closely related to RecSysTEL, although they tend to give students less autonomy to accept or reject the pathways chosen for them [Chen (2008)]. MF is particularly powerful in modeling students’ performance and knowledge, because it implicitly incorporates guess and slip factors as latent factors [Thai-Nghe et al. (2011)]. The current implementation of RiPPLE uses the recommender system of khosravi2017, which employs MF.
2.4 Reciprocal Recommendation
Reciprocal recommender systems has a literature rich with applications in online dating [Pizzato et al. (2013)], job matching [Hong et al. (2013)], and social networking [Guy (2015)], each highly tailored by the best consideration of the specific needs and constraints of the immediate environment. Fundamentally they entail operationalised user preferences and seek solutions such as a list of recommendations that match those preferences to a higher degree than competing items. In reciprocal recommendation, items are usually other users whose preferences must also be fulfilled and so entail a higher level of complexity than other recommender systems [Pizzato et al. (2010)]. Much of the research in social recommending has been developed and evaluated in existing social networks and particularly online dating sites where the site and/or the individual users may have a substantial data history which can be used to train machi