Related papers: Using Lotkaian Informetrics for Ranking in Digital…
The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence in an optimal…
Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this paper we will explore how statistical modelling of scholarship, such as…
Bibliometric indexes are customary used in evaluating the impact of scientific research, even though it is very well known that in different research areas they may range in very different intervals. Sometimes, this is evident even within a…
Purpose: This paper discusses ranking factors suitable for library materials and shows that ranking in general is a complex process and that ranking for library materials requires a variety of techniques. Design/methodology/approach: The…
This chapter outlines how search engine technology can be used in online public access library catalogs (OPACs) to help improve users experiences, to identify users intentions, and to indicate how it can be applied in the library context,…
In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search…
In 2009, Torres-Salinas & Moed proposed the use of library catalogs to analyze the impact and dissemination of academic books in different ways. Library Catalog Analysis (LCA) can be defined as the application of bibliometric techniques to…
A theoretic framework for multimedia information retrieval is introduced which guarantees optimal retrieval effectiveness. In particular, a Ranking Principle for Distributed Multimedia-Documents (RPDM) is described together with an…
In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try…
The combination of informetric analysis and information retrieval allows a twofold application. (1) While in-formetrics analysis is primarily used to gain insights into a scientific domain, it can be used to build recommen-dation or…
This paper is about a better understanding on the structure and dynamics of science and the usage of these insights for compensating the typical problems that arises in metadata-driven Digital Libraries. Three science model driven retrieval…
This work falls in the areas of information retrieval and semantic web, and aims to improve the evaluation of web search tools. Indeed, the huge number of information on the web as well as the growth of new inexperienced users creates new…
From more than half a century ago indexing scientific articles has been studied intensively to provide a more efficient data retrieval and to conserve researchers invaluable time. In the last two decades with the emergence of the World Wide…
To account for strong aging characteristics of citation networks, we modify Google's PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm,…
We submit newly developed citation impact indicators based not on arithmetic averages of citations but on percentile ranks. Citation distributions are-as a rule-highly skewed and should not be arithmetically averaged. With percentile ranks,…
A ranking is an ordered sequence of items, in which an item with higher ranking score is more preferred than the items with lower ranking scores. In many information systems, rankings are widely used to represent the preferences over a set…
Many latent (factorized) models have been proposed for recommendation tasks like collaborative filtering and for ranking tasks like document or image retrieval and annotation. Common to all those methods is that during inference the items…
We propose a new data mining approach in ranking documents based on the concept of cone-based generalized inequalities between vectors. A partial ordering between two vectors is made with respect to a proper cone and thus learning the…
We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of…
Matchmaking and information ranking are helping process for users, by offering them the best answers possible at their request. When there is no exact answer, giving them the closest proposition available is an efficient upgrade of that…