Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103395
Title: Joint implicit and explicit neural networks for question recommendation in CQA services
Authors: Tu, Hongkui
Wen, Jiahui
Sun, Aixin
Wang, Xiaodong
Keywords: Data Mining
Neural Networks
DRNTU::Engineering::Computer science and engineering
Issue Date: 2018
Source: Tu, H., Wen, J., Sun, A., & Wang, X. (2018). Joint implicit and explicit neural networks for question recommendation in CQA services. IEEE Access, 6, 73081-73092. doi:10.1109/ACCESS.2018.2881119
Series/Report no.: IEEE Access
Abstract: Community question answering (CQA) services have emerged as a type of popular social platforms. In the social network, experts provide knowledgeable answers to the questions in their domain of expertise, while celebrities publish influential opinions toward the topics led by some questions. Given the large amount of knowledge organized in the format of question–answers, an interesting research problem is to recommend questions to users so as to maximize their engagements with the platform. However, recommending questions in CQA services is a non-trivial task. Data sources in the CQA services are of different types. It is challenging to incorporate heterogeneous information for the recommendation task. Furthermore, data sparsity is an inherent problem in such platforms. In this paper, we propose a model that is able to jointly model both implicit and explicit information for question recommendation. The model integrates multiple data sources and addresses the problem of data heterogeneity. In the proposed model, we dynamically discover latent user groups and incorporate those hierarchical information to bridge the semantic gaps among users in the shared latent space. We evaluate the proposed model on two real-world datasets, and demonstrate that our model outperforms the state-of-the-art alternatives by a large margin. We also investigate different structures of the proposed model to study the effects of different data sources.
URI: https://hdl.handle.net/10356/103395
http://hdl.handle.net/10220/47297
DOI: http://dx.doi.org/10.1109/ACCESS.2018.2881119
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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