Traditionally, search engines have ignored the reading difficulty of documents and the reading proficiency of users in computing a document ranking. This is one reason why Web se...
Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. W...
A growing number of applications are built on top of search engines and issue complex structured queries. This paper contributes a customisable ranking-based processing of such qu...
We reveal that the Okapi BM25 retrieval function tends to overly penalize very long documents. To address this problem, we present a simple yet effective extension of BM25, namel...
Methods for fusing document lists that were retrieved in response to a query often use retrieval scores (or ranks) of documents in the lists. We present a novel probabilistic fusi...
Using relevance feedback can significantly improve (ad hoc) retrieval effectiveness. Yet, if little feedback is available, effectively exploiting it is a challenge. To that end,...
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. In this pos...
Guido Zuccon, Leif Azzopardi, C. J. van Rijsbergen
As context is acknowledged as an important factor that can affect users’ preferences, many researchers have worked on improving the quality of recommender systems by utilizing ...
The use of phrases in retrieval models has been proven to be helpful in the literature, but no particular research addresses the problem of discriminating phrases that are likely ...
Abstract. Traditional retrieval models assume that query terms are independent and rank documents primarily based on various term weighting strategies including TF-IDF and document...
Scientists depend on literature search to find prior work that is relevant to their research ideas. We introduce a retrieval model for literature search that incorporates a wide ...