Paper Title
Big-Data Analysis for user Query-based Recommendation

In the era of big scholarly data, user query-based recommendation is playing an increasingly signicant role as it solves information overload issues by automatically suggesting relevant references that align with researchers interests. Many state-of-the-art models have been utilized for user query-based recommendation, among which graph-based models have gar-nered signicant attention, due to their exibility in integrating rich information that inuences users preferences. Co-authorship is one of the key relations in user query-based recommendation, but it is usually regarded as a binary relation in current graph-based models. This binary modeling of co-authorship is likely to result in information loss, such as the loss of strong or weak relationships between specic research topics. To address this issue, we present a ne-grained method for co-authorship modeling that incorporates the co-author network structure and the topics of their published articles. Then, we design a three-layered graph-based recommendation model that integrates ne-grained co-authorship as well as au-thor paper, and paper keyword relations. My model effectively generates query-oriented recommendations using a simple rel-evance computation. Extensive experiments conducted on a subset of the anthology network data set for performance evaluation demonstrate that our method outperforms other models in terms of both Recall and NDCG. Keywords - Co-Authorship, Graph-Model, User Query-based Recommendation