A taxonomy of web prediction algorithms

Web prefetching techniques are an attractive solution to reduce the user-perceived latency. These techniques are driven by a prediction engine or algorithm that guesses following actions of web users. A large amount of prediction algorithms has been proposed since the first prefetching approach was published, although it is only over the last two or three years when they have begun to be successfully implemented in commercial products. These algorithms can be implemented in any element of the web architecture and can use a wide variety of information as input. This affects their structure, data system, computational resources and accuracy. The knowledge of the input information and the understanding of how it can be handled to make predictions can help to improve the design of current prediction engines, and consequently prefetching techniques. This paper analyzes fifty of the most relevant algorithms proposed along 15 years of prefetching research and proposes a taxonomy where the algorithms are classified according to the input data they use. For each group, the main advantages and shortcomings are highlighted. © 2012 Elsevier Ltd. All rights reserved.This work has been partially supported by Spanish Ministry of Science and Innovation under Grant TIN2009-08201, Generalitat Valenciana under Grant GV/2011/002 and Universitat Politecnica de Valencia under Grant PAID-06-10/2424.Domenech, J.; De La Ossa Perez, BA.; Sahuquillo Borrás, J.; Gil Salinas, JA.; Pont Sanjuan, A. (2012). A taxonomy of web prediction algorithms. Expert Systems with Applications. 39(9):8496-8502. https://doi.org/10.1016/j.eswa.2012.01.140S8496850239

The bipartite clique: A topological paradigm for Web user search customization and Web site restructuring

The objective of this dissertation research is to aid the Web user to achieve his search objective at a host Web site by organizing a strongly connected neighborhood of Web pages that are thematically and spatially related to the user\u27s search interest. Therefore, methods were developed to (1) find all Web pages at a given Web site that are thematically similar to a user\u27s initial choice of a Web page (selected from the set of Web pages returned in response to a query by any popular search engine), and (2) organize these pages hierarchically in terms of their relevance to the user\u27s initial Web page request. This selection and organization of pages is dynamically adjusted in order to make these methods responsive to the user\u27s choice of pages defining his search agenda. The methods developed in this work skillfully incorporate the production of the bipartite clique graph structure to simulate both spatial and thematic relatedness of Web pages. By ranking the user\u27s initial page choice as the most relevant page, the authority page, link analysis is used to identify a set of pages with out-links to this authority page and assemble these into a hub of relevant pages. The authority set (initially containing only the user\u27s initial page choice) is then expanded to include other pages with in-links from the set of hub pages. The authority-hub relationship signified by Web page links is used to define the two partite sets of the biclique graph. The partite set of authority pages contains the user\u27s initial page choice and other thematically and spatially similar pages. The partite set of hub pages contains pages whose out-links to the authority pages serve as validation of their thematic relevance to the user\u27s search objective. Two maximal biclique neighborhoods of Web pages specific to the user\u27s interest, containing eight and five pages respectively, were successfully extracted from Web server access logs containing 47,635 entries and 1,140 distinct request pages. The iterative use of these methods in association with three Web page metrics introduced in this research facilitated extending a neighborhood dynamically to include nine additional relevant pages