by Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps, Djoerd Hiemstra, and Maarten Marx
Relevance Feedback (RF) is a common approach for enriching queries, given a set of explicitly or implicitly judged documents to improve the performance of the retrieval. Although it has been shown that on average, the overall performance of retrieval will be improved after relevance feedback, for some topics, employing some relevant documents may decrease the average precision of the initial run. This is mostly because the feedback document is partially relevant and contains off-topic terms which adding them to the query as expansion terms results in loosing the retrieval performance. These relevant documents that hurt the performance of retrieval after feedback are called “poison pills”. In this paper, we discuss the effect of poison pills on the relevance feedback and present significant words language models (SWLM) as an approach for estimating feedback model to tackle this problem.
To be presented at the 15th Dutch-Belgian Information Retrieval Workshop, DIR 2016 on 25 November in Delft.