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Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion
Andrea Fiandro, Giorgio Crepaldi, Diego Monti, Giuseppe Rizzo, M · 2020-02-08 · via cs.IR updates on arXiv.org

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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