As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and cross-language information retrieval (CLIR) settings. The studies of RF in CLIR have been focused on query expansion ...

Personalization has been deemed one of the major challenges in information retrieval with a significant potential for providing better search experience to individual users. Especially, the need for enhanced user models better capturing elements such as ...

Traditional search evaluation approaches have often relied on domain experts to evaluate results for each query. Unfortunately, the range of topics present in any representative sample of web queries makes it impractical to have expert evaluators for ...

Relevance feedback has been demonstrated to be an effective strategy for improving retrieval accuracy. The existing relevance feedback algorithms based on language models and vector space models are not effective in learning from negative feedback ...

Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examples is to employ human experts to judge the relevance of documents. ...