Abstract: The rapid increase in the number of researchers on social network platform makes it very difficult to find academic collaborators with similar interests. This paper proposes a recommendation method for academic collaborators based on multi-features under ResearchGate platform. This method combines the three features of paper text similarity, social relevance and self-activity to measure the relationship, and uses the multi-layer perception mechanism to build a recommendation model for top-N recommendation. The text similarity is calculated by the Doc2Vec text depth representation model, and the social relevance is calculated by graph-based random walk algorithm. The experimental results show that AC-Rec is better than the existing academic collaborators recommendation methods based on ResearchGate platform. When N is 30, the hit rate reaches 53.90%, which can effectively recommend potential academic collaborators.

Key words: ResearchGate, recommendation, multi-layer perceptron, text similarity, social relevance