Explanation for User Trust in Context-Aware Recommender Systems for Search-As-Learning

Learning through web browsing, often termed Search-as-Learning (SaL), can create information overload, due to thousands of search results. SaL can be made more efficient by developing context-aware tools that recommend items to the user and minimize information overload. However, to use context-aware recommender systems (CARS) users need to trust it. Literature has proposed explanations as a feature that helps to build trust. We investigate the impact of explanation on user trust and user experience for using CARS for SaL. Our study results show that people trust a CARS without explanation more during the first use, but for a CARS with explanations, user trust is significant only after multiple uses. Through interviews, we also uncovered the interesting paradox that even though users do not perceive that explanations add to their learning outcomes, they still prefer to use a CARS with explanations over one without.

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