TL;DR
Auto-Surprise is an automated recommender system library that enhances algorithm selection and hyperparameter tuning using TPE optimization, outperforming the original Surprise library in accuracy and efficiency across multiple datasets.
Contribution
It extends the Surprise library with automated hyperparameter optimization using TPE, improving recommendation accuracy and reducing runtime.
Findings
Auto-Surprise outperforms Surprise in RMSE on multiple datasets.
It finds optimal hyperparameters faster than grid search.
Auto-Surprise achieves comparable or better accuracy with less computational cost.
Abstract
We introduce Auto-Surprise, an Automated Recommender System library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to out-of-the-box Surprise library, Auto-Surprise performs better when evaluated with MovieLens, Book Crossing and Jester Datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
