What is it about?
LLMs are a revolutionary technology, but to date, little research has examined how they can be utilized by SMEs in a cost-effective way. In this paper, we examine the application of a LLM-driven conversational recommendation system in the field with real customers.
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Why is it important?
Results show good performance in the field with real customers (85.5% recommendation accuracy), however, high latency (5.7s) and median costs ($0.04) per interaction challenge the systems viability for lower-profit margin contexts. Nevertheless, we demonstrate the feasibility of the system in a customer-facing role, helping to diffuse the innovative potential of AI down to smaller market players.
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This page is a summary of: EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context, ACM Transactions on Recommender Systems, March 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3803546.
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