AlwaysFHao/TiM4Rec
[Neurocomputing 2025] The code for the paper "TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model"
This project helps e-commerce and content platforms provide highly relevant suggestions to users. By taking a user's past interactions with items (like movies watched or products purchased) and incorporating the timing of these actions, it generates personalized recommendations for what they might like next. This is for product managers, merchandisers, and platform owners who want to improve user engagement and sales through better recommendation systems.
No commits in the last 6 months.
Use this if you manage an online platform and want to enhance your sequential recommendation system to consider the time aspect of user interactions for more accurate suggestions.
Not ideal if you are looking for a simple, out-of-the-box recommendation engine without needing to delve into model configuration and dataset preparation.
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Language
Python
License
MIT
Category
Last pushed
Aug 23, 2025
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