# Recommending Learning Objects with Arguments and Explanations
## 2 Related Work  
It has been acknowledged that online recommender approaches suffer from the cost of retraining the model, are built to optimize online performance which does not necessarily match online user behavior, and fail to capture preference drifts [7]. These challenges have boosted research towards the development of continuously learning systems, such as conversational recommender systems. Conversational Recommender Systems are usually used to quickly establish preferences for users when we don't know too much about the one we are recommending. This is especially helpful to address the cold-start recommendation problem. The problem of eliciting user preferences can be addressed through different methods like interviews-based strategies, asking the user to rate some items, active learning, interactivity, or utilizing feedback in an online setting [7; 8; 9]. Interactive recommenders give the user a more active role so that the recommender system can improve their recommendations online. There are different works that use this approach, like critique-based [10], constraint-based [11], or dialogical recommenders [12]. Our system differs from critiquing-based recommenders since users do not critique items explicitly, but the way of reasoning that the system has followed to propose those items. In addition, our system is less affected by the cold-start problem, since recommendations are based on learning theories and argumentation inferences that also apply to new users once they have logged in the system and their learning profile is established. Furthermore, all these approaches have some kind of interactivity with the user to try to learn more from him/her. However, this work will not only try to learn and modify what the system knows about the user during the conversation, but in addition it will also try to persuade the user towards the best choice using arguments and explanations. In [13], the authors present a survey where they compare 24 interactive recommender systems from the point of view of visualization, which is an effective approach to facilitate human perception and understanding of what is being recommended to the user. The authors have clustered the 24 analyzed recommenders into different clusters that focus on a feature of the recommender (transparency, justification, controllability, etc.). The feature most closely related to this work is the ability to generate justifications. Supporting justifications is usually used to explain the recommendations by comparing item tags with user preferences. Two examples are _Tagsplanation_[14] and _MovieExplain_[15]. In [16; 17], there are more examples of how to use justifications for recommender systems. The way recommendations are presented to the user influence in the explanation attributes (or metrics) that are used. These attributes are: _transparency_ (explain how the system worked), _scrutability_ (which allows users to tell the system it is wrong), _trust_ (to increase user's confidence in the system), _effectiveness_ (to help users make good decisions), _efficiency_ (to help users make decisions faster), _satisfaction_ (to help the user to enjoy the use of the system), and _persuasiveness_ (to convince users to choose the recommended item). This work focuses in the persuasiveness skills of the system to convince users by means of arguments generated during the recommender process. The literature on persuasion systems reports that users find suggestions from sources that have proven to be reliable more credible [18]. Thus, the more credible a recommender system is, the more likely are its recommendations to be accepted. However, in [19], the authors analyze how the credibility of the system can be captured by a proper selection of its features. There are different approaches to follow, but _explanations_ (i.e., arguments) is an efficient way to increase the system credibility, since they can clarify the system recommendation (the good intentions and efforts it does to provide the user the best recommendation) to users [20].