Context Style Explanation for Recommender Systems

Abstract

Recommender systems support users by helping them choose items, and explanations for the recommendations further enhance such support. Previous explanation styles were based on information about users and items, such as the demographics of users and contents of items. Contexts, such as “usage scenarios” and “accompanying persons, ” have not been used for explanations, although they influence user's choice of items. In this paper, we propose a context style explanation method, presenting contexts suitable for consuming the recommended items. The expected impacts of context style explanations are as follows: 1) persuasiveness: recognition of a suitable context for usage motivates users to consume items, and 2) usefulness: envisioning a context helps users to make the right choices because the values of items depend on contexts. We evaluate the persuasiveness and usefulness of the context-style explanation by a crowdsourcing-based user study in a restaurant recommendation setting. The context style explanation is compared to the demographic and content style explanations. We also combine the context style and other explanation styles, confirming that hybrid styles improve the persuasiveness and usefulness of the explanation. Further, we investigate the personal preferences for explanation styles and reveal how gender and age relate to such preferences. The contributions of this paper are: the proposal of the novel context style explanation method, the demonstration of the persuasiveness and usefulness of the proposed method by a user study, and the findings of gender- and age-dependence of explanation style preferences.