Hybrid Session-based News Recommendation using Recurrent Neural Networks

• URL: http://arxiv.org/abs/2006.13063v1
• Date: Mon, 22 Jun 2020 17:24:43 GMT
• Title: Hybrid Session-based News Recommendation using Recurrent Neural Networks
• Authors: Gabriel de Souza P. Moreira, Dietmar Jannach, Adilson Marques da Cunha
• Abstract summary: We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based news recommendation. Our results confirm the benefits of modeling the sequence of session clicks with RNNs.
• Score: 4.6193503399184275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based news recommendation that is able to leverage a variety of information types using Recurrent Neural Networks. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of modeling the sequence of session clicks with RNNs and leveraging side information about users and articles, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms.

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