Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges

The traditional recommendation pipeline—data collection, feature engineering, encoding, scoring, and ranking—offers limited semantic understanding and struggles with open‑domain knowledge. Large language models (LLMs) can inject external knowledge and cross‑domain reasoning, but they lack collaborative signals and incur high computational costs.

We identify five key stages where LLMs can be applied: (1) augmenting user and item profiles via prompts that generate richer textual features; (2) enhancing feature encoding by using BERT‑style models to embed user reviews and item descriptions; (3) improving scoring and ranking through zero‑shot, few‑shot, or fine‑tuned LLM inference; (4) employing LLMs as a dialogue manager to orchestrate multi‑module recommendation workflows; and (5) generating synthetic interactions for cold‑start and long‑tail scenarios.

Practical implementations include the GENRE approach for news recommendation, U‑BERT for user representation, UniSRec for item encoding, and the CTRL framework that aligns collaborative and textual modalities via contrastive learning. Experiments on MovieLens, Amazon, and Alibaba datasets show consistent AUC and LogLoss gains without increasing inference latency.

Industrial challenges remain: training efficiency, inference latency, and long‑text modeling. Solutions involve parameter‑efficient fine‑tuning, offline generation of LLM‑derived features, model quantization, and selective use of LLMs for feature engineering rather than real‑time scoring.

Future research directions point to better handling of cold‑start and long‑tail items, tighter integration of external knowledge through retrieval or tool‑calling, and more interactive, user‑driven recommendation interfaces.