http://www.cnr.it/ontology/cnr/individuo/prodotto/ID170614

On-line generation of suggestions for Web users (Contributo in atti di convegno)

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On-line generation of suggestions for Web users (Contributo in atti di convegno) (literal)

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Silvestri F.; Baraglia R.; Palmerini P.; Serranò M. (2004)
On-line generation of suggestions for Web users
in International Conference on Information Technology: Coding and Computing (ITCC'04), Las Vegas, USA, April 5-7, 2004
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Digital Information Research Foundation, 2004. (Nevada, USA, 5-7 April 2004). Proceedings, pp. 392-397. Pradipk Srimani (ed.). IEEE, 2004. Technical report (literal)

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One important class of Data Mining applications is the so-called ?Web Mining? that analyzes and extracts important and non-trivial knowledge from Web related data. Typical applications of Web Mining are represented by the personalization or recommender systems.These systems are aimed to extract knowledge from the analysis of historical information of a web server in order to improve the web site expressiveness in terms of readability and content availability. Typically, these systems are made up of two components. One, that isusually executed off-line with respect to the Web server normal operations, analyzes the server access logs in order to find a suitable categorization of users, the other, that is usually executed on-line with respect to the Web server normal operations, classifies the activerequests according to the previous off-line analysis. In this paper we propose SUGGEST 2.0 a recommender system that, differently from those proposed so far, does not make use of any off-line component. Moreover, in the last part of the paper, we analyze the quality of thesuggestions generated and the performance of our solution. To this purpose we also introduce a novel quality metric that tries to estimate the effectiveness of a recommender system as the capacity to anticipate user requests that could be issued farther in the future. (literal)

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The knowledge extracted from the analysis of historical information of a web server can be used to develop personalization or recommendation systems. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. Typically these systems are composed by two parts. One, executed offline, that analyze the server access logs in order to find a suitable categorization, and another executed online which is aimed at classifying the active requests, according to the previous offline analysis. In this paper we propose a WUM recommendation system, implemented as a module of the Apache web server, that is able to dynamically generate suggestions to pages that have not yet been visited by a user and might be of his potential interest. Differently from previously proposed WUM systems, SUGGEST 2.0 incrementally builds and maintain the historical information, without the need for an offline component, by means of a novel incremental graph partitioning algorithm. In the last part, we also analyze the quality of the suggestions generated and the performance of the module implemented. To this purpose we introduce also a new quality metric which try to estimate the effectiveness of a recommendation system as the capacity of anticipating users' requests that will be made farther in the future1 . (literal)

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