Knowledge discovery from users Web-page navigation

Shahabi, Cyrus; Zarkesh, Amir M.; Adibi, Jafar; Shah, Vishal

doi:10.1109/ride.1997.583692

Cited by 181 publications

(106 citation statements)

References 3 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…For example, we do not assume that information about the times spent by the users at each URL is available, which may be important to determine the actual interest of a web page (e.g., see [7,10]). Similarly, we do not assume that users can be identified (i.e., they are anonymous), and so it is not possible to extract user profiles.…”

Section: Related Workmentioning

confidence: 99%

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

Cadegnani

Guerra

Ilarri

et al. 2017

Transactions on Computational Collective Intelligence XXVI

View full text Add to dashboard Cite

Abstract. Institutional websites of governments, public bodies and many other organizations must publish and keep updated a large amount of information stored in thousands of web pages in order to satisfy the demands of citizens. Nowadays, citizens require high levels of quality information from public institutions in order to guarantee their transparency. In this scenario, the "search form", which is typically available in websites, does not adequately support the users, since it requires the users to explicitly express their information needs through keywords. The so-called "long tail" phenomenon, typical observed in e-commerce portals, also affects this kind of websites: not all the pages can be adequately displayed with a high level of importance, and so users with no popular requests can spend a lot of time to locate the information they need. Therefore, users need support for improving the effectiveness of their navigation. In these scenarios, the development of a recommending system predicting the next page a user wants to see in a large website has gained importance. For addressing this issue, complex models and approaches for recommending web pages that usually require to process personal user preferences have been proposed.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. So, they can be easily incorporated to current large websites to facilitate the users' navigation experience. Finally, the paper reports some comparative experiments using a real-world website to analyze the performance of the proposed approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Cadegnani

Guerra

Ilarri

et al. 2017

Transactions on Computational Collective Intelligence XXVI

View full text Add to dashboard Cite

show abstract

“…[2,3] discussed how to extract user behavior patterns. A method to cluster users with the same behavior patterns is presented in [4].…”

Section: Related Work -Extracting User Behaviormentioning

confidence: 99%

Extracting User Behavior by Web Communities Technology on Global Web Logs

Otsuka

Toyoda

Hirai

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.A lot of work has been done on extracting the model of web user behavior. Most of them target server-side logs that cannot track user behavior outside of the server. Recently, a novel way has been developed to collect web browsing histories, using the same method for determining TV audience ratings; i.e., by collecting data from randomly selected users called panels. The logs collected from panels(called panel logs) cover an extremely broad URL-space, and it is difficult to capture the global behaviors of the users. Here we utilize mining results of web community to group those URLs into easily understandable topics. We also use search keywords in search engine sites because user behavior is deeply related to search keyword according to preliminary experiments on panel logs. We develop a prototype system to extract user access patterns from the panel logs and to capture the global behavior based on web communities.

show abstract

“…The proxy forwards the requests and alters the replies by inserting dynamically two Java applets in each HTML page: the watcher and the toolbar. Watchers will send events when the pages are displayed, even when pages are loaded from a personal cache memory [17].…”

Section: Fig 3 Broadway Detailed Architecturementioning

confidence: 99%

WWW assisted browsing by reusing past navigations of a group of users

Jaczynski

Trousse

1998

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper, we present our case-based browsing advisor for the Web, called BROADWAY. BROADWAY follows a group of users during their navigations and supports an indirect collaboration to recommend Web pages to visit next. BROADWAY uses case-based reasoning to reuse precise experiences extracted from past navigations with a time-extended situation assessment, i.e. the recommendations are based mainly on the similarity of ordered sequences of past accessed documents. A first experimental evaluation shows that the system improves the information searching task.

show abstract

Knowledge discovery from users Web-page navigation

Cited by 181 publications

References 3 publications

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

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

Extracting User Behavior by Web Communities Technology on Global Web Logs

WWW assisted browsing by reusing past navigations of a group of users

Contact Info

Product

Resources

About