Naveed, Sidra; Ziegler, Jürgen:
Featuristic : An interactive hybrid system for generating explainable recommendations - Beyond system accuracy
In: 7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS) 2020 / Brusilovsky, Peter; Gemmis, Marco de; Felfernig, Alexander; Lops, Pasquale; O’Donovan, John; Semeraro, Giovanni (Eds.). - 7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS) 2020, 26 September 2020, Rio de Janeiro, Brazil, online - Aachen: RWTH Aachen, 2020 - (CEUR Workshop Proceedings ; 2682), pp. 14 - 25
2020book article/chapter in ProceedingsOA Gold
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title in English:
Featuristic : An interactive hybrid system for generating explainable recommendations - Beyond system accuracy
Author:
Naveed, SidraSCOPUS; Ziegler, JürgenSCOPUSLSF
Open Access?:
OA Gold
Scopus ID:
Note::
OA platinum
Language of text:
English
Keyword, Topic:
Explanations ; Hybrid Recommender System ; Interactive Recommending ; User Experience

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.