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

Autor:	Fiandro, Andrea, Crepaldi, Giorgio, Monti, Diego, Rizzo, Giuseppe, Morisio, Maurizio
Rok vydání:	2020
Předmět:	Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.03124 Zobrazit plný text záznamu View this record from Arxiv