关键词: 个性化站点, 用户意图, 查询推荐, 信息检索

Abstract: Personalized website rarely considers user’s search intention in retrieval process. To recommend more satisfactory results without any user feedback in personalized website retrieval, this paper proposes a keyword extraction method combining the cross entropy with word feature information, and a text ranking method assembling the cosine similarity with weighted Hamming distance. Firstly, web page text content is obtained from the requested personalized website by filtering the web page address. Secondly, based on the obtained text content, keywords which can reflect user’s retrieval intention are extracted. Thirdly, user’s intention vector model is constructed and a re-retrieval process is performed by calling the main search engine. Finally, the similarity between the user’s intention model and the re-retrieved records is computed, and the results sorted by the similarity values are returned to user. Experimental results show that the proposed method can reflect the user’s query intention and provide a notably convenient user experience.

Key words: personalized website, user intention, query recommendation, information retrieval