Hybrid Session-based News Recommendation using Recurrent Neural Networks

Adilson Marques da Cunha; Dietmar Jannach; Gabriel de Souza P. Moreira

Hybrid Session-based News Recommendation using Recurrent Neural Networks

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arxiv 2006.13063 v1 pith:T7T5KKPS submitted 2020-06-22 cs.LG cs.IRstat.ML

Gabriel de Souza P. Moreira , Dietmar Jannach , Adilson Marques da Cunha This is my paper · ORCID

classification cs.LGcs.IRstat.ML

keywords newsrecommendationsession-basedhybridinformationnetworksneuralrecurrent

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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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