A study of methods for negative relevance feedback

Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback technique can be applied to negative relevance feedback, it may not perform well due to the lack of positive examples. In this paper, we conduct a systematic study of methods for negative relevance feedback. We compare a set of representative negative feedback methods, covering vector-space models and language models, as well as several special heuristics for negative feedback. Evaluating negative feedback methods requires a test set with sufficient difficult topics, but there are not many naturally difficult topics in the existing test collections. We use two sampling strategies to adapt a test collection with easy topics to evaluate negative feedback. Experiment results on several TREC collections show that language model based negative feedb...

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Xuanhui Wang, Hui Fang, ChengXiang Zhai
• Keywords: Feedback, Information Technology, Negative Feedback, Relevance Feedback, SIGIR 2008