Expert Recommendation based on Enterprise Requirement

As the technological revolution and industrial changes continue to advance, small and medium-sized enterprises are eager to seek technical assistance from external professionals to address their technological demands. In this paper, a heterogeneous information network (HIN) that includes semantic information is proposed for expert recommendations based on enterprise demands. Firstly, we constructed a weighted HIN consisting of two kinds of vertices (papers and experts) and two kinds of relations (semantic connections, writing relations). Secondly, an improved meta-path-based weighted random wandering strategy is designed to capture wandering sequences with semantic relationships, and we adopted network representation learning methods to compute the feature representations of the nodes. Then we investigated expert long-term and short-term feature representations of the research topics, and when a demand is proposed, it would be added to the proposed heterogeneous information network based on the semantic relationship between the demand and the paper. Finally, we used a meta-path-based unbiased update algorithm to compute the vector of demand nodes and cosine similarity calculations to obtain a list of expert recommendations.