EncodeRec: An Embedding Backbone for Recommendation Systems

Abstract: Recent recommender systems increasingly leverage embeddings from large pre-trained LLMs (PLMs). However, such embeddings exhibit two key limitations: (1) PLMs are not explicitly optimized to produce structured and discriminative embedding spaces, and (2) their representations remain overly generic, often failing to capture the domain-specific semantics crucial for recommendation tasks. We present EncodeRec, an approach designed to align textual representations with recommendation objectives while learning compact, informative embeddings directly from item descriptions. EncodeRec keeps the LLM parameters frozen during recommender system training, making it computationally efficient without sacrificing semantic fidelity. Experiments across core recommendation benchmarks demonstrate its effectiveness both as a backbone for sequential recommendation models and for semantic ID tokenization, showing substantial gains over PLM-based and embedding model baselines. These results underscore the pivotal role of embedding adaptation in bridging the gap between general-purpose LLMs and practical recommender systems.