Nowadays, citizens require high level quality information from public institutions in order to guarantee their transparency. Institutional websites of governmental and public bodies must publish and keep updated a large amount of information stored in thousands of web pages in order to satisfy the demands of their users. Due to the amount of information, the “search form”, which is typically available in most such websites, is proven limited to support the users, since it requires them to explicitly express their information needs through keywords. The sites are also affected by the so-called “long tail” phenomenon, a phenomenon that is typically observed in e-commerce portals. The phenomenon is the one in which not all the pages are considered highly important and as a consequence, users searching for information located in pages that are not condiered important are having a hard time locating these pages. The development of a recommender system than can guess the next best page that a user wouild like to see in the web site has gained a lot of attention. Complex models and approaches have been proposed for recommending web pages to individual users. These approached typically require personal preferences and other kinds of user information in order to make successful predictions. In this paper, we analyze and compare three different approaches to leverage information embedded in the structure of web sites and the logs of their web servers to improve the effectiveness of web page recommendation. Our proposals exploit the context of the users’ navigations, i.e., their current sessions when surfing a specific web site. These approaches do not require either information about the personal preferences of the users to be stored and processed, or complex structures to be created and maintained. They can be easily incorporated to current large websites to facilitate the users’ navigation experience. Last but not least, the paper reports some comparative experiments using a real-world website to analyze the performance of the proposed approaches.
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|Data di pubblicazione:||2017|
|Titolo:||Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study|
|Autori:||Cadegnani, Sara; Guerra, Francesco; Ilarri, Sergio; del Carmen Rodríguez-Hernández, María; Trillo-Lado, Raquel; Velegrakis, Yannis; Amaro, Raquel|
|Digital Object Identifier (DOI):||10.1007/978-3-319-59268-8_2|
|Appare nelle tipologie:||Articolo su rivista|
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