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Found 5 papers, 1 papers with code
Dynamic Review-based Recommenders
In the present work we leverage the known power of reviews to enhance rating predictions in a way that (i) respects the causality of review generation and (ii) includes, in a bidirectional fashion, the ability of ratings to inform language review models and vice-versa, language representations that help predict ratings end-to-end.
Nonlinear Hawkes Process with Gaussian Process Self Effects
We continue the line of work of Bayesian inference for Hawkes processes, and our approach dispenses with the necessity of estimating a branching structure for the posterior, as we perform inference on an aggregated sum of Gaussian Processes.
Recurrent Point Review Models
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.
Recurrent Point Processes for Dynamic Review Models
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance.
Adiabatic Quantum Computing for Binary Clustering
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest.
