# Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships Source: [Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:08:59.042Z Authors: Chaoran Huang, Lina Yao, Xianzhi Wang, Boualem Benatallah, Shuai Zhang, Manqing Dong Published: 2018-08-03T05:55:21.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()