The study of methods for language model based positive and negative relevance feedback in information retrieval

Relevance feedback techniques are important to information retrieval (IR), which can effectively improve the performance of IR. They have been proved by many existing work. The feedback includes positive and negative relevance one. The most of the previous work using feedback have focused on positive relevance feedback and pseudo relevance feedback in IR. In recent years, some work has been done and investigated the negative relevance feedback in IR. However, this paper highlights the incorporation or integration between the language models based positive and negative relevance feedback in IR, where both types of feedback are used to modify and expand the user's query model. Our experimental results of using several TREC collections show that this method is significantly outperform the relevance feedback and pseudo relevance feedback in terms of the retrieval accuracy.