This project focuses on the implicit feedback-based recommendation ranking problem, aiming to divide the full dataset into multiple local datasets by item label weights and user label weights, and to use a hybrid Bayesian personalized ranking recommendation algorithm in the local datasets

Compared with the traditional Bayesian personalised ranking recommendation algorithm, the hybrid Bayesian personalised ranking recommendation algorithm in this paper is complemented by adding a pairwise ranking triad constructed from the scoring ranking based on the independence weight coefficient algorithm, which constitutes a hybrid pairwise ranking triad. Finally, simulated recommendation experiments are trained on Python, and the final accuracy obtained by model evaluation is about 50% and coverage is about 90%.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

If you think you can help in any of these areas or in many areas we haven't thought of yet, then please take a look at our Contributors' guidelines.

If you want to report a problem or suggest an enhancement we'd love for you to open an issue at this github repository because then we can get right on it. But you can also contact Yuki(Yuxin) by email [email protected].