CTRL-Rec: Controlling Recommender Systems With Natural Language

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 \textit{requests} (e.g., ``I want to see respectful posts with a different perspective than mine"). However, integrating these user requests into traditional recommender systems, which focus on predicting user interaction with specific items, remains a necessary challenge to overcome for practical applications. We propose a method, \textbf{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, with serving times and costs on par with contemporary recommender systems, and a performance superior than simple embedding-based retrieval.