AKGNN: Attribute Knowledge Graph Neural Networks Recommendation for Corporate Volunteer Activities

Due to the collective decision-making nature of enterprises, the process of accepting recommendations is predominantly characterized by an analytical synthesis of objective requirements and cost-effectiveness, rather than being rooted in individual interests. This distinguishes enterprise recommendation scenarios from those tailored for individuals or groups formed by similar individuals, rendering traditional recommendation algorithms less applicable in the corporate context. To overcome the challenges, by taking the corporate volunteer as an example, which aims to recommend volunteer activities to enterprises, we propose a novel recommendation model called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>ttribute <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b>nowledge <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b>eural <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b>etworks (AKGNN). Specifically, a novel comprehensive attribute knowledge graph is constructed for enterprises and volunteer activities, based on which we obtain the feature representation. Then we utilize an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</b>xtended <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b>ariational <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>uto-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b>ncoder (eVAE) model to learn the preferences representation and then we utilize a GNN model to learn the comprehensive representation with representation of the similar nodes. Finally, all the comprehensive representations are input to the prediction layer. Extensive experiments have been conducted on real datasets, confirming the advantages of the AKGNN model. We delineate the challenges faced by recommendation algorithms in Business-to-Business (B2B) platforms and introduces a novel research approach utilizing attribute knowledge graphs.