Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
This work addresses the need for more efficient and effective recommender system development for practitioners, though it is incremental as it builds directly on an existing library.
The authors tackled the problem of automating algorithm selection and hyperparameter tuning in recommender systems by introducing Auto-Surprise, an extension of the Surprise library, which improved performance on datasets like MovieLens and reduced runtime compared to baseline methods.
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.