基于TF-IQF模型和图聚类的个性化搜索研究

针对信息检索领域存在的用词歧义和检索词简短的问题,本文提出了一种基于TF-IQF模型和图聚类的个性化查询建议方法。对于用户的查询请求,提供查询建议,帮助用户进行查询修正,进而检索到其所需的信息;同时通过获取不同用户的查询偏好,以达到个性化查询推荐的目的。实验结果表明,该方法能够给出个性化的查询建议,为用户提供潜在感兴趣的资源,具有较高的准确率。

Research of Personalized Search Based on the TF-IQF Model and Graph Clustering

As for the problems that the input query are usually ambiguous and concise in the current information retrieval,this paper presents a personalized query recommendation method based on the TF-IQF model and graph clustering.It provides query recommendation for a giving query,helps the users to amend query and retrieves the information needed.Simultaneously,it will achieve the goal of providing personalized query suggestions by capturing different user preferences.The experimental results show that the method gives personalized query suggestions,provides the potential resources that the user is interested in and obtains high precision which improves the quality of personalized retrieval systems.