Internet ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including scientometrics and systemic risk in finance (SinkRank, DebtRank ...
Selection and aggregation of ranking criteria became an important topic in information retrieval as search is getting more specialized and as volume of electronically available information grows. In this context, document ranking has undergone a shift from ...
In this work we discover the daily location-driven routines which are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines which characterize both individual and group behaviors ...
Document ranking for scientific publications involves a variety of specialized resources (e.g. author or citation indexes) that are usually difficult to use within standard general purpose search engines that usually operate on large-scale heterogeneous do ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
One of the main differences between modern search engines and traditional ones is the adoption of link-based ranking algorithm in ordering Web documents. Google has claimed that it is its link-based ranking algorithm, PageRank that has made the quality of ...
There has been an increasing research interest in developing full-text retrieval based on peer-to-peer (P2P) technology. So far, these research efforts have largely concentrated on efficiently distributing an index. However, ranking of the results retrieve ...