Notes
Working notes on ranking and personalization
Interactive explainers on the parts of a recommender that are hard to picture from a formula — fusion, cold start, and how fast a user profile should forget. Every widget on these pages runs the real algorithm in your browser.
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Diversity without wrecking relevance
Maximal marginal relevance with a λ you can drag — the point where six nearly identical results become six useful ones, and the point just past it where the list stops answering the question.
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Reading a ranking metric: NDCG, MRR and the rest
Precision, recall, MRR, average precision and NDCG computed live from a list you can reorder and regrade — including the reorderings that move one metric and leave another completely still.
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Fusing two rankers without calibrating their scores
Reciprocal rank fusion, weighted RRF and CombSUM, compared in a playground where a keyword ranker and a concept ranker disagree — and you decide how to reconcile them.
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Five recommender algorithms, one catalog
Popularity, item-item CF, content-based, BPR matrix factorization and a hybrid, all ranking the same synthetic catalog for the same user — including what happens when the user is brand new.
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How fast should a user profile forget?
Exponential decay on a user profile, a half-life you can drag, and the moment where four days of debugging Kubernetes buries a year of interest in retrieval.
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