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Recurrent Point Review Models

: Cvejoski, K.; Sanchez, R.J.; Georgiev, B.; Bauckhage, C.; Ojeda, C.


IEEE Computational Intelligence Society:
International Joint Conference on Neural Networks, IJCNN 2020. Conference proceedings
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-72816-926-2
ISBN: 978-1-72816-927-9
8 S.
International Joint Conference on Neural Networks (IJCNN) <2020, Online>
Fraunhofer IAIS ()

Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal information and model how review data changes with time. Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how business or service reviews are received in time, to generate instantaneous language models with improved prediction capabilities. Simultaneously, our methodologies enhance the predictive power of our point process models by incorporating summarized review content representations. We provide recurrent network and temporal convolution solutions for modeling the review content. We deploy our methodologies in the context of recommender systems, effectively characterizing the change in preference and taste of users as time evolves. Source code is available at [1].