On the Negative Perception of Cross-domain Recommendations and Explanations

Recommender systems typically operate within a single domain, for example, recommending books based on users' reading habits. If such data is unavailable, it may be possible to make cross-domain recommendations and recommend books based on user preferences from another domain, such as movies. However, despite considerable research on cross-domain recommendations, no studies have investigated their impact on users' behavioural intentions or system perceptions compared to single-domain recommendations. Similarly, while single-domain explanations have been shown to improve users' perceptions of recommendations, there are no comparable studies for the cross-domain case.

In this article, we present a between-subject study (N=237) of users' behavioural intentions and perceptions of book recommendations. The study was designed to disentangle the eects of whether recommendations were single-or cross-domain from whether explanations were present or not. Our results show that cross-domain recommendations have lower trust and interest than single-domain recommendations, regardless of their quality. While these negative eects can be ameliorated by cross-domain explanations, they are still perceived as inferior to single-domain recommendations without explanations. Last, we show that explanations decrease interest in the single-domain case, but increase perceived transparency and scrutability in both single-and cross-domain recommendations. Our ndings oer valuable insights into the impact of recommendation provenance on user experience and could inform the future development of cross-domain recommender systems.