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AI & ML/ Writing/Tagged “ranking”

Tagged “ranking”

5 posts.

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All Model Architecture23 Training & Alignment22 Inference & Serving24 Agents & Orchestration13 Reasoning & Evaluation28 Safety, Security & Governance9 Platforms & Practice22
Platforms & Practice 24 min

BM25 Refuses to Die: The Probabilistic Relevance Framework Behind Search's Most Durable Baseline

In 2021 a benchmark of 18 retrieval datasets found that a scoring function from 1994 still beat most neural retrievers outside their training domain. BM25 is not a heuristic that got lucky. It is a deliberate approximation of a p…

classical-ir information-retrieval ranking retrieval ∑ ◫
Platforms & Practice 24 min

Query Understanding Is the Hidden Half of Search: From Broder's Taxonomy to LLM Query Rewriting

An LLM-written pseudo-document lifts BM25 by 15 nDCG@10 points on TREC DL 2019, and turns a 16 ms search into one that waits over two seconds for the model. Query understanding decides what the engine searches for before ranking …

query-understanding information-retrieval retrieval rag ∑ ◫
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 ∑ ◫
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