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Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion

by   Andrea Fiandro, et al.
Politecnico di Torino

This paper describes the solution of the POLINKS team to the RecSys Challenge 2019 that focuses on the task of predicting the last click-out in a session-based interaction. We propose an ensemble approach comprising a matrix factorization for modeling the interaction user-item, and a session-aware learning model implemented with a recurrent neural network. This method appears to be effective in predicting the last click-out scoring a 0.60277 of Mean Reciprocal Rank on the local test set.


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