A synthesized method of result merging in meta-search engine

Meta-search engine is a comprehensive search tool, which is build base on those member search engines. All result entities reported by member search engines are merged into one ranked list according to their quality. It is well knowing that the core problem in meta-search engine is how to merge the results and provide user with a more effective rank list. This paper dealt with a synthesized merging algorithm by utilizing five features to estimate the quality of each result entity. For a returned result entity, we first record its' position of the original result list. Secondly, count the number of duplications. Thirdly, calculate the similarity between query terms and result content. Fourthly, get the capacity of the search members which will be called later. Fifth, analyze whether the current entity is in line with user's interests. Wherein users' interests are obtained both according to users' browsing history and feedback. Finally, we use the linear fusion model to merge the results set and re-ranking the results list. Experimental results shown that the merging method we proposed in this paper improved the accuracy and satisfaction degree in some cases compared with member search engines and several current meta-search engines.