Graph-Based Sequential Modeling for Anonymous Session-Based Recommendation to Mitigate the Cold-Start Problem

A graph-based sequential model for anonymous session-based recommendation that mitigates the cold-start problem.

A graph-based sequential model for anonymous session-based recommendation that mitigates the cold-start problem. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are personalised recommendations ranked by relevance, data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.