Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Professional Collaborative Information Seeking: Towards Traceable Search and Creative Sensemaking Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study

Authors : Sara Cadegnani, Francesco Guerra, Sergio Ilarri, María del Carmen Rodríguez-Hernández, Raquel Trillo-Lado, Yannis Velegrakis, Raquel Amaro

Published in: Transactions on Computational Collective Intelligence XXVI

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Professional Collaborative Information Seeking: Towards Traceable Search and Creative Sensemaking
next chapter Large Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest
Footnotes
5
As described in Sect. 3.2, a session includes the pages which are visited by the same user, i.e., the same IP address and User-Agent, in 30 min.
 
Literature
1.
Balabanović, M.: Learning to surf: multiagent systems for adaptive web page recommendation. Ph.D. thesis, Stanford University, May 1998
2.
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)CrossRef
3.
Bergamaschi, S., Ferrari, D., Guerra, F., Simonini, G., Velegrakis, Y.: Providing insight into data source topics. J. Data Semant. 5(4), 211–228 (2016)CrossRef
4.
Bergamaschi, S., Guerra, F., Interlandi, M., Lado, R.T., Velegrakis, Y.: Combining user and database perspective for solving keyword queries over relational databases. Inf. Syst. 55, 1–19 (2016)CrossRef
5.
Cadegnani, S., Guerra, F., Ilarri, S., Carmen Rodríguez-Hernández, M., Trillo-Lado, R., Velegrakis, Y.: Recommending web pages using item-based collaborative filtering approaches. In: Cardoso, J., Guerra, F., Houben, G.-J., Pinto, A.M., Velegrakis, Y. (eds.) KEYSTONE 2015. LNCS, vol. 9398, pp. 17–29. Springer, Cham (2015). doi:10.​1007/​978-3-319-27932-9_​2 CrossRef
6.
Chanda, J., Annappa, B.: An improved web page recommendation system using partitioning and web usage mining. In: International Conference on Intelligent Information Processing, Security and Advanced Communication (IPAC 2015), pp. 80:1–80:6. ACM, New York (2015)
7.
Gündüz, S., Özsu, M.T.: A web page prediction model based on click-stream tree representation of user behavior. In: Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), pp. 535–540. ACM 2003
8.
Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Trans. Internet Technol. 3(1), 1–27 (2003)CrossRef
9.
Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: Fifth International Conference on Intelligent User Interfaces (IUI 2000), pp. 106–112. ACM (2000)
10.
Hernández, I., Rivero, C.R., Ruiz, D., Corchuelo, R.: A statistical approach to URL-based web page clustering. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012 Companion, pp. 525–526. ACM, New York (2012)
11.
Ittoo, A., Bouma, G., Maruster, L., Wortmann, H.: Extracting meronymy relationships from domain-specific, textual corporate databases. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds.) NLDB 2010. LNCS, vol. 6177, pp. 48–59. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-13881-2_​5 CrossRef
12.
Kazienko, P., Kiewra, M.: Integration of relational databases and web site content for product and page recommendation. In: International Database Engineering and Applications Symposium (IDEAS 2004), pp. 111–116, July 2004
13.
Kosala, R., Blockeel, H.: Web mining research: a survey. SIGKDD Explor. 2(1), 1–15 (2000)CrossRef
14.
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. CoRR, abs/1405.4053 (2014)
15.
Lieberman, H.: Letizia: an agent that assists web browsing. In: 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), vol. 1, pp. 924–929. Morgan Kaufmann (1995)
16.
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR, abs/1301.3781 (2013)
17.
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Vanderwende, L., III, H.D., Kirchhoff, K. (eds.) Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA, 9–14 June 2013, pp. 746–751. The Association for Computational Linguistics (2013)
18.
Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43(8), 142–151 (2000)CrossRef
19.
Nguyen, T.T.S., Lu, H., Lu, J.: Web-page recommendation based on web usage and domain knowledge. IEEE Trans. Knowl. Data Eng. 26(10), 2574–2587 (2014)CrossRef
20.
Nirenburg, S., Raskin, V.: Supply-side and demand-side lexical semantics. In: Viegas, E. (ed.) Breadth and Depth of Semantic Lexicons. Text, Speech and Language Technology, vol. 10, pp. 283–298. Springer, Netherlands (1999)CrossRef
21.
Peng, J., Zeng, D.: Topic-based web page recommendation using tags. In: IEEE International Conference on Intelligence and Security Informatics (ISI 2009), pp. 269–271, June 2009
22.
Shahabi, C., Zarkesh, A.M., Adibi, J., Shah, V.: Knowledge discovery from users web-page navigation. In: Seventh International Workshop on Research Issues in Data Engineering (RIDE 1997), pp. 20–29. IEEE Computer Society, April 1997
23.
Souza, T., Demidova, E., Risse, T., Holzmann, H., Gossen, G., Szymanski, J.: Semantic URL Analytics to support efficient annotation of large scale web archives. In: Cardoso, J., Guerra, F., Houben, G.-J., Pinto, A.M., Velegrakis, Y. (eds.) KEYSTONE 2015. LNCS, vol. 9398, pp. 153–166. Springer, Cham (2015). doi:10.​1007/​978-3-319-27932-9_​14 CrossRef
24.
Yang, Q., Fan, J., Wang, J., Zhou, L.: Personalizing web page recommendation via collaborative filtering and topic-aware Markov model. In: 10th International Conference on Data Mining (ICDM 2010), pp. 1145–1150, December 2010
25.
Zeng, D., Li, H.: How useful are tags? — An empirical analysis of collaborative tagging for web page recommendation. In: Yang, C.C., et al. (eds.) ISI 2008. LNCS, vol. 5075, pp. 320–330. Springer, Heidelberg (2008). doi:10.​1007/​978-3-540-69304-8_​32 CrossRef
Metadata
Title
Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study
Authors
Sara Cadegnani
Francesco Guerra
Sergio Ilarri
María del Carmen Rodríguez-Hernández
Raquel Trillo-Lado
Yannis Velegrakis
Raquel Amaro
Copyright Year
2017
Book
Transactions on Computational Collective Intelligence XXVI

Print ISBN: 978-3-319-59267-1

Electronic ISBN: 978-3-319-59268-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59268-8_2

Premium Partner

    Image Credits