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Leveraging LLM for Recommender Systems and Personalized Marketing

This story is from 2026-08-24. It is preserved in the archive; the latest stories are on the live feed.

Traditional recommender systems rely on collaborative filtering and matrix factorization to surface items. These methods work well for dense interaction data, but they struggle with cold-start items, sparse user histories, and the rich unstructured metadata that drives modern catalogs. Large langua…

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  1. 2026-08-24 05:32 · DEV Community — AI
    Leveraging LLM for Recommender Systems and Personalized Marketing

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