JiaxiWong/MIND-News-RecSys
A Deep Interest Network (DIN) implementation for MIND News Recommendation with BERT semantic warm-up.
This project helps news publishers and content platforms improve how they recommend articles to readers. It takes a reader's past viewing history and the text of new articles as input, then generates a personalized list of recommended news stories. This is designed for product managers or content strategists looking to enhance user engagement and retention on their news platform.
Use this if you need a news recommendation system that can quickly suggest relevant articles, even for brand-new stories or users with limited history.
Not ideal if your primary goal is absolute state-of-the-art overall ranking performance at the expense of computational efficiency or first-hit accuracy.
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36
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Language
Jupyter Notebook
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Last pushed
Dec 02, 2025
Commits (30d)
0
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