Motivated by the recent success of end-to-end deep neural models for ranking tasks, we
present here a supervised end-to-end neural approach for query performance prediction …

The query-performance prediction task has been described as estimating retrieval
effectiveness in the absence of relevance judgments. The expectations throughout the years …

To date, query performance prediction (QPP) in the context of content-based image retrieval
remains a largely unexplored task, especially in the query-by-example scenario, where the …

An important challenge in cluster-based document retrieval is ranking document clusters by
their relevance to the query. We present a novel cluster ranking approach that utilizes …

The cluster hypothesis states that “closely associated documents tend to be relevant to the
same requests”[45]. This is one of the most fundamental and influential hypotheses in the …

Query performance prediction (QPP) is a core information retrieval (IR) task whose primary
goal is to assess retrieval quality in the absence of relevance judgments. Applications of …

Web search engines present, for some queries, a cluster of results from the same
specialized domain (“vertical”) on the search results page (SERP). We introduce a …

We address the long standing challenge of selective cluster-based retrieval; namely,
deciding on a per-query basis whether to apply cluster-based document retrieval or …

Patients and their relatives have more chances to access their healthinformation in a form of
discharge summary. Most of them do not totally understand contents in the discharge …

Conducting a systematic review (SR) is comprised of multiple tasks:(i) collect documents
(studies) that are likely to be relevant from digital libraries (eg., PubMed),(ii) manually read …