The early fusion strategy for search result diversification
Chunlin Xu, Shengli Wu · 2017
A typical strategy for search result diversification is a two-stage process: first we use a traditional search engine to obtain a ranked list of documents, in which relevance is the only concern; then the results are re-ranked so as to promote diversity. In recent years, some researchers have investigated how to use data fusion to improve search result diversity. Corresponding to the two stages of search result diversification, we may apply data fusion at either of these two stages. All previous investigations focus on fusing results at the second stage, or fusing multiple results that are already diversified. In this paper, we investigate an alternative way of fusion, or fusing multiple results at the first stage. The fused results are diversified by a re-ranking algorithm. Experiments are carried out with three groups of results submitted to the TREC web adhoc task. We find that the proposed alternative is very good. Its performance is slightly better compared with the second stage fusion. Another advantage is it can be implemented more efficiently than the second stage fusion.