Training Encoders Need Not Be the Best Readouts: Personalized Post-Training Encoder Adaptation for Collaborative Filtering

Collaborative filtering (CF) learns user and item representations from interaction histories and ranks items by dot-product scores. From matrix factorization to graph-based models, a fixed encoder matrix maps a trainable embedding table to these representations, and the same encoder is reused at inference. Yet the encoder plays a dual role: during training it both constructs representations and shapes gradient updates to the embedding table, whereas at inference it only reads out the converged table. Convergence therefore does not imply that the training encoder is the best readout, and controlled experiments confirm that an alternative encoder can outperform reuse. A user-wise analysis further shows that each encoder row combines collaborative and preference information whose score effects depend on the learned item geometry, calling for a user-specific readout informed by the trained model. Building on this analysis, we propose **PEAR**, *Personalized post-training Encoder Adaptation for Recommendation*, a lightweight framework for linear inference encoders in CF. PEAR freezes the embedding table and final item representations, constructs two encoder bases from training interactions, fitted predictions, and item geometry, and fits at most two bounded coefficients per user, limiting adaptation capacity without learning new embeddings. Experiments across datasets, backbone encoders, and training objectives show that PEAR consistently improves recommendation performance over converged models while preserving the original dot-product scoring interface and serving cost. Code and data are provided in the supplementary material.