Gavince/MTL
学习并复现经典的推荐系统多目标任务，如：SharedBottom、ESMM、MMoE、PLE
This project helps e-commerce and content platforms build more effective recommendation systems. It takes user interaction data (like clicks, likes, purchases) and generates a single model that predicts multiple user behaviors simultaneously. Data scientists and machine learning engineers working on recommendation systems will find this useful for implementing advanced multi-task learning models.
No commits in the last 6 months.
Use this if you are building a recommendation system and want to predict several user actions (e.g., click, purchase, watch time) with a single, integrated model rather than separate ones.
Not ideal if you are looking for a plug-and-play solution for general machine learning tasks outside of recommendation systems.
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Last pushed
Jul 30, 2022
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