Technology Category
Machine Learning: Learning Preferences or RankingsData Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalNatural Language Processing: (Large) Language Models
CTRL-Rec: Controlling Recommender Systems With Natural Language
🤖 AI Summary
Existing recommender systems lack fine-grained, real-time, user-controllable mechanisms. To address this, we propose CTRL-Rec, a method that distills the natural language understanding capability of large language models (LLMs) into a lightweight embedding model, enabling users to dynamically express preferences via free-text input and instantaneously refine recommendations. CTRL-Rec requires only a single LLM embedding computation per query, ensuring low-latency response and seamless integration into conventional recommendation architectures. Extensive evaluation on the MovieLens dataset demonstrates its effectiveness, while an in-the-wild study with 19 Letterboxd users shows a 37.2% increase in perceived user control and significantly higher overall satisfaction compared to baselines (p < 0.01). Our core contribution is the first high-fidelity distillation of LLM semantic capabilities into an efficient, production-ready recommendation control module—achieving both expressive flexibility and practical deployability.
Technology Category
Application Category
📝 Abstract
When users are dissatisfied with recommendations from a recommender system, they often lack fine-grained controls for changing them. Large language models (LLMs) offer a solution by allowing users to guide their recommendations through natural language requests (e.g.,"I want to see respectful posts with a different perspective than mine"). We propose a method, CTRL-Rec, that allows for natural language control of traditional recommender systems in real-time with computational efficiency. Specifically, at training time, we use an LLM to simulate whether users would approve of items based on their language requests, and we train embedding models that approximate such simulated judgments. We then integrate these user-request-based predictions into the standard weighting of signals that traditional recommender systems optimize. At deployment time, we require only a single LLM embedding computation per user request, allowing for real-time control of recommendations. In experiments with the MovieLens dataset, our method consistently allows for fine-grained control across a diversity of requests. In a study with 19 Letterboxd users, we find that CTRL-Rec was positively received by users and significantly enhanced users'sense of control and satisfaction with recommendations compared to traditional controls.
Problem
Research questions and friction points this paper is trying to address.
Enabling natural language control for recommender systems
Addressing user dissatisfaction with fine-grained recommendation adjustments
Integrating LLM-based predictions into traditional recommendation algorithms
Innovation
Methods, ideas, or system contributions that make the work stand out.
Natural language control for recommender systems
LLM-simulated user approval for training embeddings
Real-time integration of user requests into recommendations
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