Self-monitoring for Computer Users

2008

ABSTRACT A new class of applications and web sites called personal informatics is appearing that collects personal behavioral information about users and provides access to this information to help users become more aware of their own behaviors. Interaction with personal informatics systems has two inter-dependent phases: monitoring and feedback. Users must interact with the system in at least one of the phases for users to become aware of their behavior.

Smart Innovation, Systems and Technologies, 2017

In all the modern browsers, maintaining user's web history is one of the primary tasks. Browser history will help to summarize the activity of the user during a certain period. However, current browser history is not so efficient to visualize in a user-friendly manner and also doesn't provide enough information for personalized recommendations. One of the key reason is that browsers never maintain any inter-connection between history items. Overall history is main‐ tained in a linear fashion with no information about how the user reached to a particular state. Another issue is that it is not possible to calculate how much time the user spent on any particular website using current history system. This paper provides a conceptual idea of solving these issues by providing a framework that solves this issue by introducing linked data and also describes how this can benefit in improving user experience and quality of recommendations.

Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT'09, 2009

We examine the properties of all HTTP requests generated by a thousand undergraduates over a span of two months. Preserving user identity in the data set allows us to discover novel properties of Web traffic that directly affect models of hypertext navigation. We find that the popularity of Web sites-the number of users who contribute to their traffic-lacks any intrinsic mean and may be unbounded. Further, many aspects of the browsing behavior of individual users can be approximated by log-normal distributions even though their aggregate behavior is scale-free. Finally, we show that users' click streams cannot be cleanly segmented into sessions using timeouts, affecting any attempt to model hypertext navigation using statistics of individual sessions. We propose a strictly logical definition of sessions based on browsing activity as revealed by referrer URLs; a user may have several active sessions in their click stream at any one time. We demonstrate that applying a timeout to these logical sessions affects their statistics to a lesser extent than a purely timeout-based mechanism.

We investigate prediction and discovery of user desktop activities. The techniques we explore are unsupervised. In the first part of the paper, we show that efficient many-class learning can perform well for action predic- tion in the Unix domain, significantly improving over previously published results. This finding is promis- ing for various human-computer interaction scenarios where rich predictive features of different types may be available and where there can be substantial nonsta- tionarity. In the second part, we briefly explore tech- niques for extracting salient activity patterns or motifs. Such motifs are useful in obtaining insights into user be- havior, automated discovery of (often interleaved) high- level tasks, and activity tracking and prediction.

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.

2010

For individual Web users, understanding and controlling their exchange of personal data is a very complex task as they interact, some-times unknowingly, with hundreds of dierent websites. In this paper, we present a set of tools and an experiment dedicated to monitoring a user's Web activity in order to build an observed model of his behavior in terms of the trust given to accessed websites and of the criticality of the data exchanged.

2010

While user modelling and personalisation is an ongoing area of research, it is also a mature field with work dating back more than twenty five years with no system having gained mass adoption. In this work we introduce AMS, a user modelling system that works silently in the background while users browse the internet, modelling browsing behaviour, collecting browsing data and analysing it with a view to inferring the user's interests. Prevalent issues from similar systems, such as privacy concerns or intrusion to the user's browsing experience are nicely circumvented here as we engineer the data to being contained and stored at the user's browser while only using implicit methods to collect the data. Text analytics are used to extract key terms from the raw data which is collected from pages that the user visits and a rating is applied to these terms, taking into consideration the time spent actively viewing the page with respect to the length of the page. We show how AMS is effective in surmising the user's interests, within the bounds of the evaluations that were carried out and we show how getting results from the linked data environment played a role in enhancing the user's overall experience. v This work is dedicated with love to my mother Annie and my mother-in-law Lily Contents Acknowledgments iv List of Tables x List of Figures xi Chapter 1 Introduction

2021

This paper investigates whether computer usage profiles comprised of process-, network-, mouse-, and keystroke-related events are unique and consistent over time in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication. We collected ecologically-valid computer usage profiles from 31 MS Windows 10 computer users over 8 weeks and submitted this data to comprehensive machine learning analysis involving a diverse set of online and offline classifiers. We found that: (i) profiles were mostly consistent over the 8-week data collection period, with most (83.9%) repeating computer usage habits on a daily basis; (ii) computer usage profiling has the potential to uniquely characterize computer users (with a maximum F-score of 99.90%); (iii) network-related events were the most relevant features to accurately recognize profiles (95.69% of the top features distinguishing users were network-related); and (iv) binary mo...

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.

2010

With more and more services relying on the Web to communicate with their users, the amount of information exchanged daily by an individual through various Web channels has become difficult to control. Not only have Web 2.0 applications participated to this increase from within the browser, but many other tools now rely on the Web as their basic communication infrastructure, including RSS aggregators, social network apps, update sites, etc.

Intelligent User Interfaces, 2008

Web site personalization could be immensely improved if the user's current intentions could be recognized by the surf- ing behavior. The latter can be captured in the form of events occurring in the browser, like mouse moves or opening Web pages. But which aspects of the user's behavior best con- tribute to the recognition of the task a user is

Asian CHI Symposium 2021, 2021

Computer-related behavior change is helpful for well-being. We conducted a survey to investigate three research questions and further inform the design of computer-related behavior change applications. RQ1: What do people want to change and why/how? RQ2: What applications do people use or have used, why do they work or not, and what additional support is desired? RQ3: What are helpful/unhelpful computer breaks and why? Our survey had 68 participants and three key findings. First, time management is a primary concern, but emotional and physical side-effects are also important. Second, site blockers, self-trackers, and timers are commonly used, but they are ineffective as they are easy-to-ignore and not personalized. Third, away-from-computer breaks, especially involving physical activity, are helpful, whereas on-screen breaks are unhelpful, especially when they are long, because they are not refreshing. We recommend personalized and closed-loop computer-usage behavior change support and especially encouraging off-the-computer computer breaks. CCS CONCEPTS • Human-centered computing → User studies.

International Journal of Web Information Systems, 2018

Purpose Modern Web browsers all provide a history function that allows users to see a list of URLs they have visited in chronological order. The history log contains rich information but is seldom used because of the tedious nature of scrolling through long lists. This paper aims to propose a new way to improve users’ Web browsing experience by analyzing, clustering and visualizing their browsing history. Design/methodology/approach The authors developed a system called Personal Web Library to help users develop awareness of and understand their Web browsing patterns, identify their topics of interest and retrieve previously visited Web pages more easily. Findings User testing showed that this system is usable and attractive. It found that users can easily see patterns and trends at different time granularities, recall pages from the past and understand the local context of a browsing session. Its flexibility provides users with much more information than the traditional history fun...

2007

The amount of information available online is increasing exponentially. While this information is a valuable resource, its sheer volume limits its value. Many research projects and companies are exploring the use of personalized applications that manage this deluge by tailoring the information presented to individual users. These applications all need to gather, and exploit, some information about individuals in order to be effective. This area is broadly called user profiling. This chapter surveys some of the most popular techniques for collecting information about users, representing, and building user profiles. In particular, explicit information techniques are contrasted with implicitly collected user information using browser caches, proxy servers, browser agents, desktop agents, and search logs. We discuss in detail user profiles represented as weighted keywords, semantic networks, and weighted concepts. We review how each of these profiles is constructed and give examples of projects that employ each of these techniques. Finally, a brief discussion of the importance of privacy protection in profiling is presented. general, systems that collect implicit information place little or no burden on the user are more likely to be used and, in practice, perform as well or better than those that require specific software to be installed and/or explicit feedback to be collected.

Expert Systems with Applications, 2008

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