CAJ | 학술논문

现有的标签推荐方法大多根据标签在对象中出现的次数来表示用户，标签与资源之间的关系。这种方法对标签信息的利用过于简单，导致最终的推荐结果的准确度和召回率不高。基于这个问题，提出一种采用图模型的个性化标签推荐方法，将用户、标签和资源三者的关系转换成一个三元无向图。对图中相邻顶点的处理采用一种综合的权重衡量方法，而不相邻顶点的关系采用最短路径思想得出。既考虑标签与用户的关系，又考虑标签与资源的关系给出最后的标签推荐方法。将该方法与现存的标签推荐方法做比较。实验采用的数据来自CiteULike。实验结果表明，该方法能够显著地提高推荐结果的召回率，准确性等。
현유적표첨추천방법대다근거표첨재대상중출현적차수래표시용호，표첨여자원지간적관계。저충방법대표첨신식적이용과우간단，도치최종적추천결과적준학도화소회솔불고。기우저개문제，제출일충채용도모형적개성화표첨추천방법，장용호、표첨화자원삼자적관계전환성일개삼원무향도。대도중상린정점적처리채용일충종합적권중형량방법，이불상린정점적관계채용최단로경사상득출。기고필표첨여용호적관계，우고필표첨여자원적관계급출최후적표첨추천방법。장해방법여현존적표첨추천방법주비교。실험채용적수거래자CiteULike。실험결과표명，해방법능구현저지제고추천결과적소회솔，준학성등。
The current tag recommendation methods mainly use the number of tags which appear in the object to repre-sent the relationship among user, tag and item. Using the information of tags in this way is too simple that the precision and recall of the final recommendation result are relatively low. This paper proposes a personalized tag recommendation method using graph model, which converts the relationship among user, tag and item to an undirected tripartite graph. An integrated weight measure is used to compute the adjacent vertices in the graph, while the nonadjacent vertices adopt the thought of shortest path. Tag recommendation method considers not only the relationship between tag and user, but also tag and item. This method is compared to the existing algorithms. The experiment data is collected from CiteULike. The experiment shows that the method can significantly improve the performance of recall and precision.