Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Understand

Feature engineering has long been central to recommender systems, yet effectively leveraging textual item features remains challenging.

• Recent advances in large language models (LLMs) have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood.
• Prior studies often rely on general-purpose embedding benchmarks (e.g., MTEB) when selecting LLMs, overlooking the unique characteristics of recommendation tasks.
• To address this gap, we introduce BLaIR, a comprehensive benchmark for evaluating LLMs as semantic encoders in recommendation scenarios.