Downsampling recommender training data can cut runtime and estimated carbon emissions by roughly 18 to 52 percent, with performance losses that vary strongly by algorithm, dataset, and downsampling design.
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
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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.
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Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling
Downsampling recommender training data can cut runtime and estimated carbon emissions by roughly 18 to 52 percent, with performance losses that vary strongly by algorithm, dataset, and downsampling design.