Fusing Search Results from Possible Alternative Queries

Data fusion has been shown to be a simple and effective way to improve retrieval results. Most existing data fusion methods combine ranked lists from different retrieval functions for a single given query—but in most real search settings, the diversity of retrieval functions required to achieve good fusion performance is not available. This paper presents a method for data fusion based on combining ranked lists from different queries that users could have entered for their information need, keeping the retrieval function fixed. We argue that if we can obtain a set of "possible queries" for an information need, we can achieve high effectiveness by fusing the rankings over the possible queries. In order to demonstrate effectiveness, we present experimental results on 5 different datasets covering tasks such as ad-hoc search, novelty and diversity search, and search in the presence of implicit user feedback. Our results show strong performances for our method, it is competitive with state-of-the-art methods on the same datasets, and in some cases outperforms them.