An incremental learning strategy for search results optimization

The traditional search engines rarely consider features of the document set, so the retrieval results are not so satisfactory after new documents are added into the retrieval system. In this paper we combine the features of document set with traditional retrieval models and propose an incremental learning strategy to optimize the retrieval results. We got a feature thesaurus by extracting the document set. Then we collected some new features from the newly added documents and refreshed the feature thesaurus. Finally, the search results were reordered according to how well they matched the feature thesaurus with a query. Several parts of experiments show that this method averagely rises by 9.4% in precision, 14.9% in MAP, 4.6% in DCG towards the top 10 results than traditional retrieval means, which means that it processes better while making a query, even better while querying to the newly added documents, and faster while locating the required information.