Eelco Herder

Web page revisitation revisited: implications of a long-term click-stream study of browser usage

This paper presents results of an extensive long-term click-stream study of Web browser usage. Focusing on character and challenges of page revisitation, previous findings from seven to thirteen years ago are updated. The term page re-visit had to be differentiated, since the recurrence rate--the key measure for the share of page revisits--turns out to strongly depend on interpretation. We identify different types of revisitation that allow assessing the quality of current user support and developing concepts for new tools. Individual navigation strategies differ dramatically and are strongly influenced by personal habits and type of site visited. Based on user action logs and interviews, we distinguished short-term revisits (backtrack or undo) from medium-term (re-utilize or observe) and long-term revisits (rediscover). We analyze current problems and provide suggestions for improving support for different revisitation types.

Not quite the average: An empirical study of Web use

In the past decade, the World Wide Web has been subject to dramatic changes. Web sites have evolved from static information resources to dynamic and interactive applications that are used for a broad scope of activities on a daily basis. To examine the consequences of these changes on user behavior, we conducted a long-term client-side Web usage study with twenty-five participants. This report presents results of this study and compares the user behavior with previous long-term browser usage studies, which range in age from seven to thirteen years. Based on the empirical data and the interview results, various implications for the interface design of browsers and Web sites are discussed. A major finding is the decreasing prominence of backtracking in Web navigation. This can largely be attributed to the increasing importance of dynamic, service-oriented Web sites. Users do not navigate on these sites searching for information, but rather interact with an online application to complete certain tasks. Furthermore, the usage of multiple windows and tabs has partly replaced back button usage, posing new challenges for user orientation and backtracking. We found that Web browsing is a rapid activity even for pages with substantial content, which calls for page designs that allow for cursory reading. Click maps provide additional information on how users interact with the Web on page level. Finally, substantial differences were observed between users, and characteristic usage patterns for different types of Web sites emphasize the need for more adaptive and customizable Web browsers.

Cross-system user modeling and personalization on the Social Web

In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start.

Interweaving public user profiles on the web

While browsing the Web, providing profile information in social networking services, or tagging pictures, users leave a plethora of traces In this paper, we analyze the nature of these traces We investigate how user data is distributed across different Web systems, and examine ways to aggregate user profile information Our analyses focus on both explicitly provided profile information (name, homepage, etc.) and activity data (tags assigned to bookmarks or images) The experiments reveal significant benefits of interweaving profile information: more complete profiles, advanced FOAF/vCard profile generation, disclosure of new facets about users, higher level of self-information induced by the profiles, and higher precision for predicting tag-based profiles to solve the cold start problem.

Linkage, aggregation, alignment and enrichment of public user profiles with Mypes

Today, users leave a plethora of traces on the Web. They fill traditional profile information at social networking services, write articles in their personal blogs or annotate social media content in resource sharing systems such as Flickr or Delicious. In this paper we present the so-called Mypes service that connects, aggregates, aligns and enriches profile information form diverse online services and makes the bundled profiles available for end-users and third-party applications. It enables end-users to overview their distributed profile data traces and provides application developers means for integrating aggregated profile data into their applications to feature personalization. Our evaluation reveals the benefits of Mypes: it generates semantically rich user profiles with high precision.

A Comparison of Paper-Based and Online Annotations in the Workplace

While reading documents, people commonly make annotations: they underline or highlight text and write comments in the margin. Making annotations during reading activities has been shown to be an efficient method for aiding understanding and interpretation. In this paper we present a comparison of paper-based and online annotations in the workplace. Online annotations were collected in a laboratory study, making use of the Web-based annotation tool SpreadCrumbs. A field study was out to gather paper-based annotations. The results validate the benefits of Web annotations. A comparison of the online annotations with paper-based annotations provides several insights in user needs for enhanced online annotation tools, from which design guidelines can be drawn.

Personalized Adaptation to Device Characteristics

Device characteristics, such as screen size and means of interaction, and the context in which a device is used, seriously affect the users mental representation of an information environment and its intended use. We hypothesize that user characteristics are valuable resources for determining which information is of interest in specific situations. Our project goal is to design mechanisms for adapting navigation support to device characteristics and its context of use, thereby considering that user goals and the resulting expected navigation behavior might be subject to change.

Beyond the usual suspects: context-aware revisitation support

A considerable amount of our activities on the Web involves revisits to pages or sites. Reasons for revisiting include active monitoring of content, verification of information, regular use of online services, and reoccurring tasks. Browsers support for revisitation is mainly focused on frequently and recently visited pages. In this paper we present a dynamic browser toolbar that provides recommendations beyond these usual suspects, balancing diversity and relevance. The recommendation method used is a combination of ranking and propagation methods. Experimental outcomes show that this algorithm performs significantly better than the baseline method. Further experiments address the question whether it is more appropriate to recommend specific pages or rather (portal pages of) Web sites. We conducted two user studies with a dynamic toolbar that relies on our recommendation algorithm. In this context, the outcomes confirm that users appreciate and use the contextual recommendations provided by the toolbar.

The impact of bookmarks and annotations on refinding information

Refinding information has been interwoven with web activity since its early beginning. Even though all common web browsers were equipped with a history list and bookmarks early enough to facilitate this need, most users typically use search engines to refind information. However, both bookmarks and search based tools have significant limitations that impact their usability: the former are known to be hard to manage over the course of time, whereas the latter require the user to recall a specific combination of keywords or context. Most importantly, though, both are particularly inappropriate in cases where a piece of information is contained within an unstructured web page. In this paper, we present in-context annotation as a more efficient alternative to these methodologies. To verify this claim, we conducted a study in which we compare the performance of experienced users in all three approaches while revisiting specific pieces of information in the web after a long period of time. The outcomes suggest that in-context annotation clearly outperforms both traditional strategies.

Interactive web usage mining with the navigation visualizer

Web usage mining, the analysis of user navigation paths through web sites, is a common technique for evaluating site designs or adaptive hypermedia techniques. However, often it is hard to relate aggregated clusters or measures to actual user navigation behavior. By contrast, basic graph-based visualizations of user navigation paths are easier to interpret, but it is difficult to find effective views that convey all the required information. In this paper we present the Navigation Visualizer, a web usage analysis tool that combines the two approaches. The Navigation Visualizer makes use of the rich data set that is collected by the Scone proxy-based web enhancement framework and facilitates dynamic selection of the data and interactive exploration with various layout mechanisms, color codings and markers. Several aggregated measures can be calculated and exported to statistical and data mining packages.