On Natural Language User Profiles for Transparent and Scrutable Recommendation

TL;DR

This paper explores using natural language to represent user preferences in recommendation systems to enhance transparency, control, and portability, moving beyond traditional implicit data reliance.

Contribution

It proposes a novel approach of employing natural language user profiles for recommendation systems to improve transparency and user control.

Findings

Natural language profiles can improve transparency.

Language-based profiles enable better user control.

Potential reduction in reliance on implicit data.

Abstract

Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people's understanding and control of these systems and explore a fundamentally new way of thinking about representation of knowledge in recommendation and personalization systems. Specifically, we argue that it may be both desirable and possible for algorithms that use natural language representations of users' preferences to be developed. We make the case that this could provide significantly greater transparency, as well as affordances for practical actionable interrogation of, and control over, recommendations. Moreover, we argue that such an approach, if successfully applied, may enable a major step towards systems that rely less on noisy implicit observations while increasing portability of knowledge of one's interests.