Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships

TL;DR

This paper introduces a tensor factorization framework that leverages hierarchical topical relationships to accurately identify and recommend domain experts across multiple collaborative networks, enhancing knowledge sharing.

Contribution

It presents a novel tensor-based method incorporating hierarchical regularization to improve expert identification in multi-topic question answering communities.

Findings

Effective expert ranking across diverse topics

Improved accuracy over baseline methods

Robust performance on Stack Exchange data

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.