# Recommending Learning Objects with Arguments and Explanations
## 2 Related Work
As has been presented above, some attributes or metrics help the user to trust more on recommendations. That is, they better accept recommendations if they understand how they were generated by inspecting how the system works (transparency), or if they can understand and evaluate why recommendations can be suitable to them (education). Furthermore, it has been shown that even if the recommended items are already known, users still prefer the system to provide explanations [21]. Explanations may also avoid bad choices. As explained in [17], the possibility of generating a certain type (style) of explanations depends largely on the algorithm being used and its ability to produce the information needed to generate these explanations. The styles of explanations that C-ERS can generate are directly conditioned by the type of knowledge that the rules of the system handle (collaborative, content-based, and knowledge-based). Research in ERS has been very active in recent years [22; 23; 24; 25; 26]. Providing recommendation techniques with insights and explanations to learners and educators has become an important research line [1]. Argumentation theory and their tools have shown many successes in educational domains, especially regarding the training of critical thinking for law students [27]. Recommender systems combined with computational argumentation techniques have reported successful applications to suggest music [28], news [29], movies [30], or content-based web search [31]. Among the proposed systems, closer similarities exist between our approach and those proposed in [32], a recommender system that uses defeasible logic programming to recommend movies, and [33], a products recommender system based on the [PERSON]'s argumentation theory. In the former,the authors resolve possible attacks between arguments by defining a pre-established criterion of preference between rules. However, our system uses a probabilistic method to calculate the probability of one argument prevailing over another, which makes the system more adaptable. In the latter, the system allows for generating arguments to support the recommendation of specific items (cameras) and their features. However, the [PERSON]'s model only allows for generate claims in favor or against a specific conclusion (the recommendation of the item), while our system allows for generating different explanation styles from content-based, knowledge-based, and collaborative information sources and that allow for critiquing the explanation itself and not only the recommendation. Although the impact of explanations on the performance of recommender systems has been proved [19], few research has been focused on the domain of educational recommender systems. Designing recommender systems for TEL environments presents several challenges [34]. For instance, each student learns following a different learning process, with a variety of learning methods and tools. Our work involves a contribution in these areas by proposing a conversational ERS provided with an argumentation framework that makes the system able to engage in a conversation with students to help them to understand the underlying logic of the system, to elicit drifts in their preferences, and to provide effective recommendations.