Search results optimization method combined with multi-features

The optimization of search results has always been the research hotspot in the area of search engine. More concretely, topic partition by clustering proved to be a good way. However, the clusters, some of which still contain a lot of documents, implicitly limit the users´ retrieval speed. Meanwhile we find that the information of documents´ features have good effects on the document ranking. To address the issue, we try to apply the multi-features to search results after the process of clustering. Statistic and semantic information of the multi-features are fully used to re-rank the documents. Related experiments show that our approach outperforms that of single clustering much. The evaluation indicators´ rising shows that the Top N results satisfy the users´ need more.