Annually, web search engine providers spend more and more money on documents ranking in search engines result pages (SERP). Click models provide advantageous information for ranking documents in SERPs through modeling interactions among users and search engines. Here, three modules are employed to create a hybrid click model; the first module is a PGM-based click model, the second module in a d...

An expert search system assists users with their “expertise need” by suggesting people with relevant expertise to their query. Most systems work by ranking documents in response to the query, then ranking the candidates using information from this initial document ranking and known associations between documents and candidates. In this paper, we aim to determine whether we can approximate an ev...

The effective ranking of documents in search engines is based on various document features, such as the frequency of the query terms in each document, the length, or the authoritativeness of each document. In order to obtain a better retrieval performance, instead of using a single or a few features, there is a growing trend to create a ranking function by applying a learning to rank technique ...

Evidence based on web graph structure is reportedly used by the current generation of World-Wide Web (WWW) search engines to identify “high-quality”, “important” pages and to reject “spam” content. However, despite the apparent wide use of this evidence its application in web-based document retrieval is controversial. Confusion exists as to how to incorporate web evidence in document ranking, a...

Recently, there is increased interest in searching and computing the similarity between Electronic Medical Records (EMRs). A unique characteristic of EMRs is that they consist of ontological concepts derived from biomedical ontologies such as UMLS or SNOMEDCT. Medical researchers have found that it is effective to search and find similar EMRs using their concepts, and have proposed sophisticate...

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP’s limitations. All alternatives deviate from the PRP by incorporating dependencies. This results in a re-ranking that promotes or demotes documents depending upon their relationship with the documents that have been already ranked. In this paper,...

This is an overview of the NTCIR-9 INTENT task, which comprises the Subtopic Mining and the Document Ranking subtasks. The INTENT task attracted participating teams from seven different countries/regions – 16 teams for Subtopic Mining and 8 teams for Document Ranking. The Subtopic Mining subtask received 42 Chinese runs and 14 Japanese runs; the Document Ranking subtask received 24 Chinese runs...

This article discusses a new document indexing scheme for information retrieval. For a structured (e.g., scientific) document, Pasi et al. proposed varying weights to different sections according to their importance in the document. This concept is extended here to unstructured documents. Each sentence in a document is initially assigned weights (significance in the document) with the help of a...

This paper concerns document ranking in information retrieval. In information retrieval systems, the widely accepted probability ranking principle (PRP) suggests that, for optimal retrieval, documents should be ranked in order of decreasing probability of relevance. In this paper, we present a new document ranking paradigm, arguing that a better, more general solution is to optimize top-n ranke...