Large Language Model-Based Recommendation

The field studies how language models can use item descriptions, interaction histories, and conversational feedback to predict and explain what people may want. Common approaches include instruction-tuned or reinforcement-trained recommenders, retrieval-augmented generation, semantic item IDs, and hybrids that combine language representations with collaborative filtering or graph signals. Work spans sequential and conversational recommendation, cold-start retrieval, and large-scale search and feeds, with distillation and offline feature generation used to reduce serving costs. A central tension is that semantic reasoning and fluent explanations do not guarantee accurate, grounded recommendations: candidate retrieval, user-data alignment, bias, safety, and latency remain important challenges.