Abstract in English:

Hybrid recommender systems (RS) have shown to improve system accuracy by combining benefits from the collaborative filtering (CF) and content-based (CB) approaches. Recently, the increasing complexity of such algorithms has fueled a demand for researchers to focus more on the user-oriented aspects such as explainability, user interaction, and control mechanisms. Even in cases, where explanations are provided, the systems mostly fall short in explaining the connection between the recommended items and users’ preferred features. Additionally, in most cases, rating or re-evaluating items is typically the only option for users to specify or manipulate their preferences. With the purpose to provide advanced explanations, we implemented a prototype system called Featuristic, by applying a hybrid approach that uses content-features in a CF approach and exploits feature-based similarities. Addressing important user-oriented aspects, we have integrated interactive mechanisms into the system to improve both preference elicitation and preference manipulation. Besides, we have integrated explanations for the recommendations into these interactive mechanisms. We evaluated our prototype system in two user studies to investigate the impact of the interactive explanations on the user-oriented aspects. The results showed that the Featuristic System with interactive explanations have significantly improved users’ perception of the system in terms of the preference elicitation, explainability, and preference manipulation – compared to the systems that provide non-interactive explanations.