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Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
August 19, 2020 Β· Declared Dead Β· π ACM Conference on Recommender Systems
"No code URL or promise found in abstract"
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Authors
Rohan Anand, Joeran Beel
arXiv ID
2008.13532
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG,
stat.ML
Citations
21
Venue
ACM Conference on Recommender Systems
Last Checked
4 months ago
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.
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