Category: recommender-systems
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Not All RecSys Problems Are Created Equal
Not All RecSys Problems Are Created Equal How baseline strength, churn, and subjectivity determine complexity The post Not All RecSys Problems Are Created Equal appeared first on Towards Data Science. Diogo Leitão Go to original source
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Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes
Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes How much of your AI agent’s output is real data versus confident guesswork? The post Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes appeared first on Towards Data Science. James Barney Go to original source
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How Convolutional Neural Networks Learn Musical Similarity
How Convolutional Neural Networks Learn Musical Similarity Learning audio embeddings with contrastive learning and deploying them in a real music recommendation app The post How Convolutional Neural Networks Learn Musical Similarity appeared first on Towards Data Science. Luke Stuckey Go to original source
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Why MAP and MRR Fail for Search Ranking (and What to Use Instead)
Why MAP and MRR Fail for Search Ranking (and What to Use Instead) MAP and MRR look intuitive, but they quietly break ranking evaluation. Here’s why these metrics mislead—and how better alternatives fix it. The post Why MAP and MRR Fail for Search Ranking (and What to Use Instead) appeared first on Towards Data Science.…
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Scaling Recommender Transformers to a Billion Parameters
Scaling Recommender Transformers to a Billion Parameters How to implement a new generation of transformer recommenders The post Scaling Recommender Transformers to a Billion Parameters appeared first on Towards Data Science. Kirill Кhrylchenko Go to original source
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Introducing Univariate Exemplar Recommenders: how to profile Customer Behavior in a single vector
Introducing Univariate Exemplar Recommenders: how to profile Customer Behavior in a single vector Customer Profiling Surveying and improving the current methodologies for customer profiling ***To understand this article, knowledge of embeddings, clustering, and recommendation systems is required. The implementation of this algorithm has been released on GitHub and is fully open-source. I am open to…