Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
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1Lehmann, J. ; Castillo, C. ; Lalmas, M. ; Baeza-Yates, R.: Story-focused reading in online news and its potential for user engagement.
In: Journal of the Association for Information Science and Technology. 68(2017) no.4, S.869-883.
Abstract: We study the news reading behavior of several hundred thousand users on 65 highly visited news sites. We focus on a specific phenomenon: users reading several articles related to a particular news development, which we call story-focused reading. Our goal is to understand the effect of story-focused reading on user engagement and how news sites can support this phenomenon. We found that most users focus on stories that interest them and that even casual news readers engage in story-focused reading. During story-focused reading, users spend more time reading and a larger number of news sites are involved. In addition, readers employ different strategies to find articles related to a story. We also analyze how news sites promote story-focused reading by looking at how they link their articles to related content published by them, or by other sources. The results show that providing links to related content leads to a higher engagement of the users, and that this is the case even for links to external sites. We also show that the performance of links can be affected by their type, their position, and how many of them are present within an article.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23707/full.
Anmerkung: This work was done while Janette Lehmann was a PhD student at Universitat Pompeu Fabra and it was carried out as part of her PhD internship at Yahoo! Labs Barcelona. This work was carried out while Carlos Castillo was working at Qatar Computing Research Institute.
2Castillo, C. ; Baeza-Yates, R.: Web retrieval and mining.
In: Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates. London : Taylor & Francis, 2009. S.xx-xx.
Abstract: The advent of the Web in the mid-1990s followed by its fast adoption in a relatively short time, posed significant challenges to classical information retrieval methods developed in the 1970s and the 1980s. The major challenges include that the Web is massive, dynamic, and distributed. The two main types of tasks that are carried on the Web are searching and mining. Searching is locating information given an information need, and mining is extracting information and/or knowledge from a corpus. The metrics for success when carrying these tasks on the Web include precision, recall (completeness), freshness, and efficiency.
Anmerkung: Vgl.: http://www.tandfonline.com/doi/book/10.1081/E-ELIS3.
3Baeza-Yates, R. ; Boldi, P. ; Castillo, C.: Generalizing PageRank : damping functions for linkbased ranking algorithms.
In: http://chato.cl/papers/baeza06_general_pagerank_damping_functions_link_ranking.pdf [Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR) Conference, SIGIR'06, August 6-10, 2006, Seattle, Washington, USA].
Abstract: This paper introduces a family of link-based ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family. The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, link-based importance propagation, from the way propagation decays over paths. We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data. Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank's using a fixed small number of iterations; comparisons were performed using Kendall's tau on large domain datasets.