Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion
February 08, 2020 Β· Declared Dead Β· π arXiv.org
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Authors
Andrea Fiandro, Giorgio Crepaldi, Diego Monti, Giuseppe Rizzo, Maurizio Morisio
arXiv ID
2002.03124
Category
cs.IR: Information Retrieval
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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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