mquad/sars_tutorial
Repository for the tutorial on Sequence-Aware Recommender Systems held at TheWebConf 2019 and ACM RecSys 2018
This project provides a hands-on guide for building and evaluating recommender systems that learn from sequences of user interactions, not just individual preferences. It takes raw user interaction data (like purchase history or viewing order) and demonstrates how to generate more intelligent, sequence-aware recommendations. This is ideal for data scientists, machine learning engineers, and researchers working on personalizing user experiences in domains like e-commerce, content streaming, or online services.
344 stars. No commits in the last 6 months.
Use this if you need to understand and implement recommender systems that factor in the order and sequence of user actions to provide more relevant suggestions.
Not ideal if you are looking for a plug-and-play recommender system solution without diving into the underlying algorithms or if you only need basic, non-sequence-aware recommendations.
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Python
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MIT
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Jan 27, 2021
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