jiwidi/Behavior-Sequence-Transformer-Pytorch
This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
This project helps e-commerce and content platforms recommend items by understanding user behavior patterns. It takes a history of a user's interactions with items, like movies they've rated or products they've viewed, and predicts their potential interest or rating for a new, target item. This is ideal for product managers, data scientists, or recommendation engineers looking to improve personalized suggestions.
176 stars. No commits in the last 6 months.
Use this if you need to build a recommendation system that leverages a user's past sequence of interactions to predict their future preferences for specific items.
Not ideal if you're looking for a recommendation system that doesn't rely on sequential user behavior data, such as content-based filtering or basic collaborative filtering.
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Jul 11, 2022
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