Open Access

Christian Scheel, Angel Castellanos, Therbin Lee, Ernesto William De Luca

• Recommender Systems refer to those applications that offer contents or items to the users, based on their previous activity. These systems are broadly used in several fields and applications, being common that an user interact with several recommender systems during his daily activities. However, most of these systems are black boxes which users really don’t understand how to work. This lack of transparency often causes the distrust of the users. A suitable solution is to offer explanations to the user about why the system is offering such recommendations. This work deals with the problem of retrieving and evaluating explanations based on hybrid recommenders. These explanations are meant to improve the perceived recommendation quality from the user’s perspective. Along with recommended items, explanations are presented to the user to underline the quality of the recommendation. Hybrid recommenders should express relevance by providing reasons speaking for a recommended item. In this work we present an attribute explanation retrievalRecommender Systems refer to those applications that offer contents or items to the users, based on their previous activity. These systems are broadly used in several fields and applications, being common that an user interact with several recommender systems during his daily activities. However, most of these systems are black boxes which users really don’t understand how to work. This lack of transparency often causes the distrust of the users. A suitable solution is to offer explanations to the user about why the system is offering such recommendations. This work deals with the problem of retrieving and evaluating explanations based on hybrid recommenders. These explanations are meant to improve the perceived recommendation quality from the user’s perspective. Along with recommended items, explanations are presented to the user to underline the quality of the recommendation. Hybrid recommenders should express relevance by providing reasons speaking for a recommended item. In this work we present an attribute explanation retrieval approach to provide these reasons and show how to evaluate such approaches. Therefore, we set up an online user study where users were asked to provide movie feedback. For each rated movie we additionally retrieved feedback about the reasons this movie was liked or disliked. With this data, explanation retrieval can be studied in general, but it can also be used to evaluate such explanations.…