Recommender systems are becoming must-have facilities on e-commerce websites to alleviate information overload and to improve user experience. One important component of such systems is the explanations of the recommendations. Existing explanation approaches have been classified by style and the classes are aligned with the ones for recommendation approaches, such as collaborative-based and content-based. Thanks to the semantically interconnected data, knowledge graphs have been boosting the development of content-based explanation approaches. However, most approaches focus on the exploitation of the structured semantic data to which recommended items are linked (e.g. actor, director, genre for movies). In this paper, we address the under-studied problem of leveraging knowledge graphs to explain the recommendations with items' unstructured textual description data. We point out 3 shortcomings of the state of the art entity-based explanation approach: absence of entity filtering, lack of intelligibility and poor user-friendliness. Accordingly, 3 novel approaches are proposed to alleviate these shortcomings. The first approach leverages a DBpedia category tree for filtering out incorrect and irrelevant entities. The second approach increases the intelligibility of entities with the classes of an integrated ontology (DBpedia, schema.org and YAGO). The third approach explains the recommendations with the best sentences from the textual descriptions selected by means of the entities. We showcase our approaches within a tourist tour recommendation explanation scenario and present a thorough face-to-face user study with a real commercial dataset containing 1310 tours in 106 countries. We showed the advantages of the proposed explanation approaches on five quality aspects: intelligibility, effectiveness, efficiency, relevance and satisfaction.
Lully, V., Laublet, P., Stankovic, M., & Radulovic, F. (2018). Enhancing explanations in recommender systems with knowledge graphs. In Procedia Computer Science (Vol. 137, pp. 211–222). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.09.020