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Found 3 papers, 1 papers with code
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
In pursuit of an answer, we exhaustively evaluate the predictive performance of 250 selection strategies besides selecting the top-n. We extensively evaluate each selection strategy over twelve implicit feedback and eight explicit feedback data sets with eleven recommender systems algorithms.
The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case Study
We compared six different feature sets with varying quantities of features which were generated from the baseline data and evaluated on a total of 19 RecSys algorithms, baseline ML algorithms, Automated Machine Learning (AutoML) pipelines, and state-of-the-art RecSys algorithms that incorporate side information.
Detecting Handwritten Mathematical Terms with Sensor Based Data
The second classifier is a Deep Neural Network that combines convolution layers with recurrent layers to predict windows with a single label, out of the 15 possible classes, at an F1 score of >60%.
