Profiling users for question answering communities via flow-based constrained co-embedding model

Liang, S., Luo, Y. and Meng, Z. (2022) Profiling users for question answering communities via flow-based constrained co-embedding model. ACM Transactions on Information Systems, 40(2), 34. (doi: 10.1145/3470565)

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In this article, we study the task of user profiling in question answering communities (QACs). Previous user profiling algorithms suffer from a number of defects: they regard users and words as atomic units, leading to the mismatch between them; they are designed for other applications but not for QACs; and some semantic profiling algorithms do not co-embed users and words, leading to making the affinity measurement between them difficult. To improve the profiling performance, we propose a neural Flow-based Constrained Co-embedding Model, abbreviated as FCCM. FCCM jointly co-embeds the vector representations of both users and words in QACs such that the affinities between them can be semantically measured. Specifically, FCCM extends the standard variational auto-encoder model to enforce the inferred embeddings of users and words subject to the voting constraint, i.e., given a question and the users who answer this question in the community, representations of the users whose answers receive more votes are closer to the representations of the words associated with these answers, compared with representations of whose receiving fewer votes. In addition, FCCM integrates normalizing flow into the variational auto-encoder framework to avoid the assumption that the distributions of the embeddings are Gaussian, making the inferred embeddings fit the real distributions of the data better. Experimental results on a Chinese Zhihu question answering dataset demonstrate the effectiveness of our proposed FCCM model for the task of user profiling in QACs.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Meng, Dr Zaiqiao
Authors: Liang, S., Luo, Y., and Meng, Z.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:ACM Transactions on Information Systems
Publisher:ACM Press
ISSN (Online):1558-2868
Published Online:24 November 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in ACM Transactions on Information Systems 40(2): 34
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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