Predicting the next page a user wants to see in a large website has gained importance along the last decade due to the fact that the Web has become the main communication media between a wide set of entities and users. This is true in particular for institutional government and public organization websites, where for transparency reasons a lot of information has to be provided. The “long tail” phenomenon affects also this kind of websites and users need support for improving the effectiveness of their navigation. For this reason, complex models and approaches for recommending web pages that usually require to process personal user preferences have been proposed. In this paper, we propose three different approaches to leverage information embedded in the structure of web sites and their logs to improve the effectiveness of web page recommendation by considering the context of the users, i.e., their current sessions when surfing a specific web site. This proposal does not require either information about the personal preferences of the users to be stored and processed or complex structures to be created and maintained. So, it can be easily incorporated to current large websites to facilitate the users’ navigation experience. Experiments using a real-world website are described and analyzed to show the performance of the three approaches.

Recommending Web Pages Using Item-Based Collaborative Filtering Approaches / Cadegnani, Sara; Guerra, Francesco; Ilarri, Sergio; R. o. d. r. i. g. u. e. z. Hernandez, Marıa del Carmen; Trillo Lado, Raquel; Velegrakis, Yannis. - STAMPA. - 9398:(2015), pp. 17-29. (Intervento presentato al convegno 1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015 tenutosi a Coimbra nel 8-9 September 2015) [10.1007/978-3-319-27932-9_2].

Recommending Web Pages Using Item-Based Collaborative Filtering Approaches

GUERRA, Francesco;
2015

Abstract

Predicting the next page a user wants to see in a large website has gained importance along the last decade due to the fact that the Web has become the main communication media between a wide set of entities and users. This is true in particular for institutional government and public organization websites, where for transparency reasons a lot of information has to be provided. The “long tail” phenomenon affects also this kind of websites and users need support for improving the effectiveness of their navigation. For this reason, complex models and approaches for recommending web pages that usually require to process personal user preferences have been proposed. In this paper, we propose three different approaches to leverage information embedded in the structure of web sites and their logs to improve the effectiveness of web page recommendation by considering the context of the users, i.e., their current sessions when surfing a specific web site. This proposal does not require either information about the personal preferences of the users to be stored and processed or complex structures to be created and maintained. So, it can be easily incorporated to current large websites to facilitate the users’ navigation experience. Experiments using a real-world website are described and analyzed to show the performance of the three approaches.
2015
1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015
Coimbra
8-9 September 2015
9398
17
29
Cadegnani, Sara; Guerra, Francesco; Ilarri, Sergio; R. o. d. r. i. g. u. e. z. Hernandez, Marıa del Carmen; Trillo Lado, Raquel; Velegrakis, Yannis...espandi
Recommending Web Pages Using Item-Based Collaborative Filtering Approaches / Cadegnani, Sara; Guerra, Francesco; Ilarri, Sergio; R. o. d. r. i. g. u. e. z. Hernandez, Marıa del Carmen; Trillo Lado, Raquel; Velegrakis, Yannis. - STAMPA. - 9398:(2015), pp. 17-29. (Intervento presentato al convegno 1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015 tenutosi a Coimbra nel 8-9 September 2015) [10.1007/978-3-319-27932-9_2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1082352
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