资源论文Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

2019-09-30 | |  74 |   51 |   0

 Abstract Aiming to represent user characteristics and personal interests, the task of user profifiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user pro- fifiling as a classifification task, where each user is formulated as an individual data instance. Nevertheless, a user’s profifile is not only reflflected from her/his affifiliated data, but also can be inferred from other users, e.g., the users that have similar copurchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profifiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. Through such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profifiling

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