januverma/transformers-for-sequential-recommendation
Notebooks on using transformers for sequential recommendation tasks
This project helps e-commerce managers, content strategists, or anyone managing user-facing recommendations improve their systems. By analyzing the sequence of items a user interacts with (like products viewed, articles read, or videos watched), it generates more relevant, personalized recommendations for their next interaction. This helps personalize user experiences and can increase engagement.
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Use this if you manage a system that recommends items to users and want to leverage the power of sequential behavior to suggest more personalized next steps.
Not ideal if your recommendation needs are simple, based only on static user preferences, or if you don't have sequential user interaction data available.
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Jupyter Notebook
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Last pushed
Jan 10, 2023
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