一种改进的个性化查询引文推荐方法

为充分利用文本内容的上下文信息,结合图模型及查询向量的构建方法,提出一种融合查询内容信息的个性化引文推荐方法。通过三种论文信息构建三层图模型,并在不同层上设置不同参数,调整节点向不同层次的跳转概率;利用word2vec技术构建的查询向量,可以有效利用文本上下文内容信息,使相似的文章在距离上更加接近,进而对候选文章进行评分预测与论文推荐。在Association of Computational Linguistics Anthology Network数据集上进行计算分析,相同查询下与原有的方法相比在recall@N上平均提高约7%,在NDCG@N上平均提高约11%。实验结果表明该方法可以使引文推荐的质量得到有效的提升,能够获得较好的推荐效果。

Improved method for personalized query citation recommendation

To 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.