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Research on search results optimization technology with category features integration
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AI
DOI：10.1007/s13042-011-0037-9.png)
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Abstract
En 中文
The optimization of search results has always been the research hot spot in the area of search engine. In previous work, various kinds of document ranking were used to optimize the search results, in which topic partition by clustering has been proved to be a good way. However, the clusters, containing a lot of documents unorganized, still directly limit the retrieval speed. To address this issue, the paper firstly integrates the two methods together to re-rank the documents in clusters. We find that the category features, which have great discernibility for categories, have good effects on the document sequencing. Thereupon we attempt to apply the category features into search results on the basis of the clusters. Related experiments show that our Top N results are more in line with the users' needs and the retrieval speed can be implicitly improved, which proves that our approach significantly outperforms the original clustering method.
Keywords:
Category features
Search results
Optimization
Clustering
Re-ranking
Journal
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