December 2024 monthly summary: Focused on strengthening model evaluation capabilities in TorchRec. Delivered the HindsightTargetPR metric to evaluate model performance using precision and recall at defined thresholds, including a bucketized variant for finer granularity and improved calibration in metric reporting. Implemented in the pytorch/torchrec repository with commit 33349ec73bbb55703a149e6ad07cdf22cf10184e (PR #2627). This work enables more informative evaluation for recommender systems, supports data-driven optimization, and improves calibration across predictions. Technologies demonstrated include Python, PyTorch, metric design, and collaborative PR workflow.

November 2024 — TorchRec delivered new evaluation metrics CaliFree and Unweighted NE, enabling normalized entropy-based evaluation for recommender systems. These metrics provide deeper insights into model performance by accounting for prediction-label distributions and uniform weighting, improving benchmarking and tuning decisions. The work was implemented and integrated in TorchRec with commit ec6a5a8d4b4c5b8d82e7564b211d5ed03403260d (PR #2540), aligning with our ongoing focus on robust evaluation and data-driven decision making.