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Published in AAAI, 2018
We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering.
Published in NeurIPS, 2018
To model and exploit the dynamics of missingness, we propose a latent variable named “user intent” to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process
Published in IJCAI, 2019
We propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages user’s multiple historical sessions in their behavior sequences.
Published in KDD, 2020
In this paper, we use a multi-view representation alignment approach to address this issue. Particularly, we propose a multi-task multi-view graph representation learning framework (M2GRL) to learn node representations from multi-view graphs for web-scale recommender systems.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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