一种改进的个性化查询引文推荐方法

o make full use of the context information of the papers, combined with the construction method of graph model and query vector, this paper proposed a fusion query information personalized citation recommendation method. Built a three layer graph model through three kinds of paper information, and set different parameters on different layers to adjust the jump probability of nodes to different levels; the query vector constructed using word2vec technology can effectively use the text context information, so that similar papers are closer to the distance, and then the candidate papers are predicted and recommended. Computational analyzes performed on the Association of Computational Linguistics Anthology Network dataset showed an average increase of about 7% over recall@N and an average increase of about 11% over NDCG@N for the same query compared to the original method. Experimental results show that the proposed method can effectively improve the quality of citation recommendation and get better recommendation results.