Exploring Exploration in Recommender Systems: Where? How Much? For Whom?

Abstract

Recommender systems focus on automatically surfacing suitable items for users from digital collections that are too large for the user to oversee themselves. A considerable body of work exists on surfacing items that match what a user liked in the past; this way, the recommender system will exploit its knowledge of a user's comfort zone. However, application scenarios exist in which it is explicitly important to offer the user opportunities to explore items beyond their existing comfort zones. In such cases, the recommender should include items for which there is less existing evidence that the user will like them. This calls for the recommender to explore to what extent a user would be tolerant to exploration. In this thesis, we consider that different users will likely have different preferences with respect to item exploration. We propose personalized item filtering techniques for this, modeled under a multi-armed bandit framework, that consider (1) how and where exploration vs. exploitation items should be distributed in a result overview and (2) how adventurous exploration items can be, considering a user's general willingness to explore and existing familiarity with item categories in the collection. We present the results of a survey and an online quantitative experiment with 43 users of Muziekweb, a public music library that encourages taste broadening in the Netherlands, demonstrating the effectiveness of both our proposed filtering techniques that aim for personalized exploration.