Profiling the User: A Ringside View

Lecture Notes in Computer Science, 2003

People display regularities in almost everything they do. This paper proposes characteristics of an idealized algorithm that would allow an automatic extraction of web user profil based on user navigation paths. We describe a simple predictive approach with these characteristics and show its predictive accuracy on a large dataset from KDD-Cup web logs (a commercial web site), while using fewer computational and memory resources. To achieve this objective, our approach is articulated around three notions: (1) Applying probabilistic exploration using Markov models. (2) Avoiding the problem of Markov model highdimensionality and sparsity by clustering web documents, based on their content, before applying the Markov analysis. (3) Clustering Markov models, and extraction of their gravity centers. On the basis of these three notions, the approach makes possible the prediction of future states to be visited in k steps and navigation sessions monitoring, based on both content and traversed paths.

IEEE Internet Computing, 2005

To help address pressing problems with information overload, researchers have developed personal agents to provide assistance to users in navigating the Web. To provide suggestions, such agents rely on user profiles representing interests and preferences, which makes acquiring and modeling interest categories a critical component in their design. Existing profiling approaches have only partially tackled the characteristics that distinguish user profiling from related tasks. The authors' technique generates readable user profiles that accurately capture interests, starting from observations of user behavior on the Web.

Web users surf the WEB sites quite frequently for accessing frequently required information. There is no specific information stored in the WEB logs with which a user is uniquely identified. Every user navigates the WEB site leading to certain patterns of surfing. The web users being novice can only be recognised through patterns of surfing the web. Many algorithms have been presented in the literature that considers clustering the users based on some criteria generally leading to finding favourite links. The algorithms do not use any navigational patterns and the favourite links provided in the side bars do not provide any useful information. User behaviour cannot be predicated using just the favourite links. Most of the algorithms take more time to form clusters and present them to the user the favourite links in a side bar. This paper presents a method that quickly recognizes frequent navigational patterns of the novice user derived from a WEB log, and clicks rendered by the user for navigating through URL links. The user's interestingness is captured and used to find the interesting patterns. The customised URLs are presented as toolbar icons using which the user can directly surf the interesting areas. The method has been built into large web site and it has been proved that the surfing has been made efficient when compared with other methods presented in the literature.

2007

Browsing activities are an important source of information to build profiles of the user interests and personalize the humancomputer interaction during information seeking tasks. Visited pages are easily collectible, e.g., from browsers' histories and toolbars, or desktop search tools, and they often contain documents related to the current user needs. Nevertheless, menus, advertisements or pages that cover multiple topics affect negatively the advantages of an implicit feedback technique that exploits these data to build and keep updated user profiles. This work describes a technique to collect text relevant to the current needs from sequences of pages visited by the user. The evaluation shows how it outperforms other techniques that consider the whole page contents. We also introduce an improvement based on machine learning techniques that is currently under evaluation.

2009

We perform an analysis of the way individual users navigate in the Web. We focus primarily in the temporal patterns of they return to a given page. The return probability as a function of time as well as the distribution of time intervals between consecutive visits are measured and found to be independent of the level of activity of single users. The results indicate a rich variety of individual behaviors and seem to preclude the possibility of defining a characteristic frequency for each user in his/her visits to a single site.

Computer Science and Information Systems, 2006

This paper addresses a problem of personalized information delivery related to the Web, that is based on user profiling. Different approaches to user profiling have been developed. When the user profiling is used for personalization in the context of Web, we can talk about Web personalization. There are three main groups of approaches: content-based filtering collaborative filtering and Web usage mining. We provide an overview of them including recent research results in the area with especial emphases on user profiling in the perspective of Semantic Web applications.

Expert Systems with Applications, 2008

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2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013

The investigation of the browsing behavior of users provides useful information to optimize web site design, web browser design, search engines offerings, and online advertisement. This has been a topic of active research since the Web started and a large body of work exists. However, new online services as well as advances in Web and mobile technologies clearly changed the meaning behind "browsing the Web" and require a fresh look at the problem and research, specifically in respect to whether the used models are still appropriate. Platforms such as YOUTUBE, NETFLIX or LAST.FM have started to replace the traditional media channels (cinema, television, radio) and media distribution formats (CD, DVD, Blu-ray). Social networks (e.g., Facebook) and platforms for browser games attracted whole new, particularly less tech-savvy audiences. Furthermore, advances in mobile technologies and devices made browsing "on-the-move" the norm and changed the user behavior as in the mobile case browsing is often being influenced by the user's location and context in the physical world. Commonly used datasets, such as web server access logs or search engines transaction logs, are inherently not capable of capturing the browsing behavior of users in all these facets. DOBBS (DERI Online Behavior Study) is an effort to create such a dataset in a non-intrusive, completely anonymous and privacy-preserving way. To this end, DOBBS provides a browser addon that users can install, which keeps track of their browsing behavior (e.g., how much time they spent on the Web, how long they stay on a website, how often they visit a website, how they use their browser, etc.). In this paper, we outline the motivation behind DOBBS, describe the add-on and captured data in detail, and present some first results to highlight the strengths of DOBBS.

International Journal of Computer Applications, 2010

This paper discusses primary design issues of an intelligent browser for novice users. This work focuses on the study of intelligent interface agents which adapt to the user according to the interactions between the user and the browser. We propose a personal web browser which adapts to the user interface layout and navigation. Adaptation of user interface is associated with including both layout and functionality to the user's ability and preference but generally keeping consistency. Adaptation to navigation on the other hand refers to personalization of web pages so as to provide personalized information space.

Proceedings of the Fourth International Conference on Web Information Systems and Technologies, 2008

The automatic recognition of a user's current task by the surfing behavior requires detailed knowledge of the relationship between task and behavior. An exploratory study was conducted where 20 participants performed exercises on a given Web site. These exercises corresponded to the predefined user tasks Fact Finding, Information Gathering and Just Browsing following present research on user activities. The resulting behavior was recorded in detailed event log files which contain every action performed in the browser, such as mouse moves and clicks, scrolling, the use of the back button etc. The analysis of variance indicates that the three tasks can be differentiated with a combination of selected behavioral attributes.