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arXiv · 1808.01092

Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships

Chaoran Huang·Lina Yao·Xianzhi Wang·Boualem Benatallah·Shuai Zhang·Manqing Dong
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Abstract

Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Communities offer an opportunity for sharing knowledge at a low cost, where communities users, many of whom are domain experts, can potentially provide high-quality solutions to a given problem. In this paper, we propose a framework for finding experts across multiple collaborative networks. We employ the recent techniques of tree-guided learning (via tensor decomposition), and matrix factorization to explore user expertise from past voted posts. Tensor decomposition enables to leverage the latent expertise of users, and the posts and related tags help identify the related areas. The final result is an expertise score for every user on every knowledge area. We experiment on Stack Exchange Networks, a set of question answering websites on different topics with a huge group of users and posts. Experiments show our proposed approach produces steady and premium outputs.

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Chaoran Huang, Lina Yao, Xianzhi Wang, Boualem Benatallah, Shuai Zhang, Manqing Dong. 2018-08-03. Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships. https://arxiv.org/abs/1808.01092

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