From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration
- URL: http://arxiv.org/abs/2605.02902v1
- Date: Mon, 30 Mar 2026 14:06:54 GMT
- Title: From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration
- Abstract summary: We present Red-Rec, an AI-supported exploration interface for this middle ground.<n>The design is motivated by a formative study which found that users often recognize feed staleness but struggle to articulate alternatives.<n>We evaluated Red-Rec in a mixed-design lab study against three comparison conditions: a passive feed, search, and a user-initiated chat interface.
- Score: 9.226285729379294
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
Related papers
- Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship [0.0]
Conversational AI companions have grown prominent in public discourse, yet scholarly understanding of user experiences remains limited.<n>This study offers three contributions: first, participants reported gratifications mapping onto established Uses and Gratifications (U&G) categories but qualitatively inflected by conversational AI's distinctive affordances.<n>Second, several gratifications, creative collaboration as relational co-production, relational simulation as interpersonal training, do not map onto existing typologies, instead emerging through interactive processes in which users actively simulate experiences with AI.<n>Third, gratifications shifted over time, moving from instrumental entry points toward emotional engagement and
arXiv Detail & Related papers (2026-04-07T19:53:58Z) - PromptHelper: A Prompt Recommender System for Encouraging Creativity in AI Chatbot Interactions [19.138748420166557]
We introduce prompt recommender systems (PRS) as an interaction approach that supports exploration, suggesting contextually relevant follow-up prompts.<n>We present PromptHelper, a PRS prototype integrated into an AI chatbots that surfaces semantically diverse prompt suggestions while users work on real writing tasks.<n>Results show that PromptHelper significantly increases users' perceived exploration and expressiveness without increasing cognitive workload.
arXiv Detail & Related papers (2026-01-22T01:44:51Z) - User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal [59.120335322495436]
We analyze user feedback in the user-LLM conversation logs, providing insights into when and why such feedback occurs.<n>Second, we study harvesting learning signals from such implicit user feedback.
arXiv Detail & Related papers (2025-07-30T23:33:29Z) - Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models [54.85405423240165]
We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics.<n>We implement interactive reasoning in Hippo, a prototype for AI-assisted decision making in the face of uncertain trade-offs.
arXiv Detail & Related papers (2025-06-30T10:00:43Z) - NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search [108.42163676745085]
We envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search.<n> NExT-Search integrates two complementary modes: User Debug Mode, which allows engaged users to intervene at key stages; and Shadow User Mode, where a personalized user agent simulates user preferences.
arXiv Detail & Related papers (2025-05-20T17:59:13Z) - Blending Queries and Conversations: Understanding Tactics, Trust, Verification, and System Choice in Web Search and Chat Interactions [0.8397730500554048]
This paper presents a user study where participants used an interface combining Web Search and a Generative AI-Chat feature to solve health-related information tasks.<n>We study how people behaved with the interface, why they behaved in certain ways, and what the outcomes of these behaviours were.
arXiv Detail & Related papers (2025-04-07T14:59:55Z) - Mind the Gap! Static and Interactive Evaluations of Large Audio Models [55.87220295533817]
Large Audio Models (LAMs) are designed to power voice-native experiences.<n>This study introduces an interactive approach to evaluate LAMs and collect 7,500 LAM interactions from 484 participants.
arXiv Detail & Related papers (2025-02-21T20:29:02Z) - Social Commonsense-Guided Search Query Generation for Open-Domain
Knowledge-Powered Conversations [66.16863141262506]
We present a novel approach that focuses on generating internet search queries guided by social commonsense.
Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation.
arXiv Detail & Related papers (2023-10-22T16:14:56Z) - The HCI Aspects of Public Deployment of Research Chatbots: A User Study,
Design Recommendations, and Open Challenges [19.965388973809336]
We report on a mixed-methods user study conducted on a recent research chat.
We find that abstract anthropomorphic representation for the agent has a significant effect on user's perception, that offering AI explainability may have an impact on feedback rates, and that two (diegetic and extradiegetic) levels of the chat experience should be intentionally designed.
arXiv Detail & Related papers (2023-06-07T20:24:43Z) - From Implicit to Explicit feedback: A deep neural network for modeling
sequential behaviours and long-short term preferences of online users [3.464871689508835]
Implicit and explicit feedback have different roles for a useful recommendation.
We go from the hypothesis that a user's preference at a time is a combination of long-term and short-term interests.
arXiv Detail & Related papers (2021-07-26T16:59:20Z) - Advances and Challenges in Conversational Recommender Systems: A Survey [133.93908165922804]
We provide a systematic review of the techniques used in current conversational recommender systems (CRSs)
We summarize the key challenges of developing CRSs into five directions.
These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
arXiv Detail & Related papers (2021-01-23T08:53:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.