From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration

TL;DR

This paper introduces Red-Rec, an AI-supported exploration interface designed to assist users with vague intent by summarizing feed patterns, offering exploration options, and blending new content, thereby enhancing content discovery.

Contribution

The paper presents Red-Rec, a novel AI-driven interface that proactively supports users in exploring content when they feel stuck in repetitive feeds, improving exploration and serendipity.

Findings

Red-Rec increased exploration breadth compared to passive feeds and search.

Participants relied mainly on options rather than typing, reducing effort.

Red-Rec improved serendipity ratings over other interfaces.

Abstract

Recommendation feeds work well when people are simply browsing, and search works well when they can formulate a query. Between these two cases is a common but poorly supported state: users feel that their feed has become repetitive, yet cannot clearly specify what they want instead. We refer to this state as vague intent. We present Red-Rec, an AI-supported exploration interface for this middle ground. After a period of browsing, the system summarizes patterns in the current feed (e.g., dominant content categories and possible latent interests), offers clickable exploration options, asks at most one follow-up question, and then gradually blends new content into the feed. The design is motivated by a formative study which found that users often recognize feed staleness but struggle to articulate alternatives, suggesting the need for proactive and low-effort interaction.We evaluated Red-Rec in a mixed-design lab study against three comparison conditions: a passive feed, search, and a user-initiated chat interface. Compared with user-initiated chat, Red-Rec led to broader exploration, higher serendipity ratings, and lower interaction effort. Participants in the AI-initiated condition typed very little , relying mainly on option selection, whereas participants in the user-initiated chat condition typed substantially more . We discuss how proactive, option-based AI support can help users move beyond repetitive feeds without undermining their sense of control, and we outline design implications for recommendation interfaces that support open-ended exploration.