Mar 31, 2020 · In this paper, we propose a multi-task learning framework, called MTRec, for recommendation over HIN. MTRec relies on self-attention mechanism to learn the ...
Experimental results demonstrate the superiority of MTRec over state-of-the-art. HIN-based recommendation models, and the case studies we provide illustrate ...
In this paper, we summarize our multi-task learning framework MTRec for recommendation over HIN. MTRec relies on the self-attention mechanism to learn the ...
Traditional recommender systems (RS) only consider homogeneous data and cannot fully model heterogeneous information of complex objects and relations.
MTRec relies on self-attention mechanism to learn the semantics of meta-paths in HIN and jointly optimizes the tasks of both recommendation and link prediction.
A multi-task learning framework, called MTRec, for recommendation over HIN, which relies on self-attention mechanism to learn the semantics of meta-paths in ...
In [24], a multi-task recommendation model with matrix factorization is proposed which jointly learns to give rating estimation and recommendation explanation.
In this work, we propose a multi-task graph learning framework that jointly learns from supervised and unsupervised objectives with heterogeneous graphs.
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In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN.
Aug 22, 2024 · We propose a novel framework for EHR modeling, namely MulT-EHR (Multi-Task EHR), which leverages a heterogeneous graph to mine the complex relations and model ...