Research On Citation Recommendation Problem Based On Attributed Network Representation Learning

According to statistics,thousands of academic papers are published every day.Such a large number of papers have caused an increasing information overload problem.Citation Recommendation(CR)focus on recommending references to the query paper intelligently has attracted more and more attention.We can use the paper' diversified information to solve CR problem.Hence,using network paradigm to model CR can capture papers' semantic and structural information.Recently,network representation learning based CR has received much attention.But it is still a challenging problem to couple papers' semantic and structural features fully.Due to the unique nature of CR,the author node don't associate attributes directly,so the network constructed has local nodes missing attributes.Existing attributed network representation learning models are difficult to apply directly to the CR.Therefore,the existing works mainly focus on using network topology to learn papers' features,and few works have been devoted to explore the attributed network representation learning based CR.In this dissertation,papers' semantic and structural information are effectively fused through an attributed network,and semantic links are constructed based on the nodes' semantic similarity.Semantic links are an extension of the attribute network's edge set and refer to the fact that paper nodes on the network share similar text content and provide valuable semantic supervision in the process of network representation learning.To overcome the high computational complexity and memory consumption problem,the hierarchical network representation learning technology is used to solve this problem.The main works of this dissertation are as follows:First,to solve the problem that it is difficult to fully couple papers' semantic and structural features and consider diversified information,this dissertation proposes an attributed network representation learning containing semantic links based citation recommendation algorithm(CR-ANRSL).Firstly,we construct semantic links on the attributed network according to the similarity of papers' text attributes.Semantic links are able to attract semantically strongly related paper nodes and enhance the semantic information on the network.Then,the nodes' feature representations are learned by the basic single-granular network representation learning model.Finally,a linear fusion method with multi-mode similarities is designed for measuring papers' similarity to produce a recommendation list.Experimental results on two common data sets,AAN and DBLP,show that semantic links can effectively integrate the attribute information of nodes and provide valuable supervision during the process of network representation learning,and outperform a variety of widely used CR algorithms on recall and NDCG.Second,due to the computational complexity and huge memory consumption,the single-granular network representation learning models are difficult to be applied to the large-scale networks.For this problem,this dissertation further proposes the citation recommendation algorithm based on hierarchical attributed network representation learning containing semantic links(CR-HANRSL).Firstly,the network is repeatedly coarsened into smaller networks based on the semantic relevance of the node's attributes and author's relationship to maintain the historical collaboration relationship while taking into account papers' semantic contents.And after coarsening,the original nodes' text attributes are fused to hypernode for computing semantic links.The basic network representation learning model is applied to the coarse network and the node vectors are obtained by refining coarse networks' feature representations with graph convolutional network.Finally,the multi-modal similarities between papers are fused to generate the recommendation list.Experimental results show that CR-HANRSL greatly improves the execution efficiency with almost no loss of accuracy compared with the method that appears in chapter 3.