Tagged “recommender-systems”

4 posts.

All Model Architecture23 Training & Alignment22 Inference & Serving24 Agents & Orchestration13 Reasoning & Evaluation28 Safety, Security & Governance9 Platforms & Practice22

Training & Alignment 24 min

Learning From the Log: Off-Policy Policy Learning, From IPS to Counterfactual Risk Minimisation

An unbiased estimate of every policy's value does not give you an unbiased choice of policy. The moment an optimiser searches over importance-weighted estimates, it goes looking for the estimator's noise, and the history of learn…

policy-learning-and-ope off-policy-evaluation counterfactual importance-sampling ∑ ◫

Platforms & Practice 24 min

Reading Its Own Handwriting: Position Bias, Feedback Loops and Counterfactual Correction

Move a document from rank 5 to rank 1 and its click-through rate jumps without its relevance changing at all. Train on those clicks and the model learns position, which the previous ranker chose, which means it learns to imitate …

ranking recommender-systems learning-to-rank counterfactual ∑ ◫

Model Architecture 28 min

The Generative Turn in Recommender Systems: Three Changes Wearing One Name

Kuaishou replaced its entire retrieve-rank cascade with one decoder and reported running it at 10.6 percent of the old system's operating expense. A controlled comparison of the same model family found near-zero recall on cold-st…

recommender-systems retrieval ranking scaling ∑ ◫

Reasoning & Evaluation 23 min

The Progress Illusion in Recommender Systems: Weak Baselines, Sampled Metrics and Leaky Splits

In 2019 a careful team could reproduce only 7 of 18 neural recommenders from top venues, and 6 of those 7 lost to nearest-neighbour heuristics. The models were not the problem. The protocol was: untuned baselines, metrics compute…

recommender-systems evaluation metrics benchmarks ∑ ◫