Recommending web pages using item-based collaborative filtering approaches

Sara Cadegnani; Francesco Guerra; Sergio Ilarri; María del Carmen Rodríguez-Hernández; Raquel Trillo-Lado; Yannis Velegrakis

doi:10.1007/978-3-319-27932-9_2

Recommending web pages using item-based collaborative filtering approaches

Sara Cadegnani
, Francesco Guerra^*
, Sergio Ilarri
, María del Carmen Rodríguez-Hernández
, Raquel Trillo-Lado
, Yannis Velegrakis

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers
Editors	Yannis Velegrakis, Jorge Cardoso, Jorge Cardoso, Alexandre Miguel Pinto, Francesco Guerra, Geert-Jan Houben
Publisher	Springer
Pages	17-29
Number of pages	13
ISBN (Print)	9783319279312
DOIs	https://doi.org/10.1007/978-3-319-27932-9_2
Publication status	Published - 1 Jan 2015
Event	1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015 - Coimbra, Portugal Duration: 8 Sept 2015 → 9 Sept 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9398
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015
Country/Territory	Portugal
City	Coimbra
Period	8/09/15 → 9/09/15

Funding

The authors would like to acknowledge networking support by the ICT COST Action IC1302 KEYSTONE - Semantic keyword-based search on structured data sources (www.keystone-cost.eu). We also thank the support of the CICYT project TIN2013-46238-C4-4-R and DGA-FSE.

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10.1007/978-3-319-27932-9_2

Cite this

Cadegnani, S., Guerra, F., Ilarri, S., Rodríguez-Hernández, M. D. C., Trillo-Lado, R., & Velegrakis, Y. (2015). Recommending web pages using item-based collaborative filtering approaches. In Y. Velegrakis, J. Cardoso, J. Cardoso, A. M. Pinto, F. Guerra, & G.-J. Houben (Eds.), Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers (pp. 17-29). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9398). Springer. https://doi.org/10.1007/978-3-319-27932-9_2

Cadegnani, Sara ; Guerra, Francesco ; Ilarri, Sergio et al. / Recommending web pages using item-based collaborative filtering approaches. Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. editor / Yannis Velegrakis ; Jorge Cardoso ; Jorge Cardoso ; Alexandre Miguel Pinto ; Francesco Guerra ; Geert-Jan Houben. Springer, 2015. pp. 17-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{15f334831d5e4e63a7b1beea7adeba50,

title = "Recommending web pages using item-based collaborative filtering approaches",

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{\textquoteright} navigation experience. Experiments using a real-world website are described and analyzed to show the performance of the three approaches.",

author = "Sara Cadegnani and Francesco Guerra and Sergio Ilarri and Rodr{\'i}guez-Hern{\'a}ndez, \{Mar{\'i}a del Carmen\} and Raquel Trillo-Lado and Yannis Velegrakis",

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doi = "10.1007/978-3-319-27932-9\_2",

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booktitle = "Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers",

note = "1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015 ; Conference date: 08-09-2015 Through 09-09-2015",

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Cadegnani, S, Guerra, F, Ilarri, S, Rodríguez-Hernández, MDC, Trillo-Lado, R & Velegrakis, Y 2015, Recommending web pages using item-based collaborative filtering approaches. in Y Velegrakis, J Cardoso, J Cardoso, AM Pinto, F Guerra & G-J Houben (eds), Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9398, Springer, pp. 17-29, 1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015, Coimbra, Portugal, 8/09/15. https://doi.org/10.1007/978-3-319-27932-9_2

Recommending web pages using item-based collaborative filtering approaches. / Cadegnani, Sara; Guerra, Francesco; Ilarri, Sergio et al.
Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. ed. / Yannis Velegrakis; Jorge Cardoso; Jorge Cardoso; Alexandre Miguel Pinto; Francesco Guerra; Geert-Jan Houben. Springer, 2015. p. 17-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9398).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Guerra, Francesco

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AU - Rodríguez-Hernández, María del Carmen

AU - Trillo-Lado, Raquel

AU - Velegrakis, Yannis

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AB - 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.

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BT - Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers

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Cadegnani S, Guerra F, Ilarri S, Rodríguez-Hernández MDC, Trillo-Lado R, Velegrakis Y. Recommending web pages using item-based collaborative filtering approaches. In Velegrakis Y, Cardoso J, Cardoso J, Pinto AM, Guerra F, Houben GJ, editors, Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. Springer. 2015. p. 17-29. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27932-9_2

Recommending web pages using item-based collaborative filtering approaches

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