Maximilian Viermetz

Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining

The rapid growth of the Internet has pushed the research and development of Web usage mining ever more into focus. Web usage mining and its applications have become critical to the business world. These analyses rest in turn on the ability to develop a clear understanding of the actions a user has taken. So far, the temporal order of clicks has been taken to be equal to the structural order of a session. With the advent of the newest browser generation where the use of multiple tabs has become a common feature, the above assumption does not necessarily hold any more. It is crucial to understand how the use of multiple tabs impacts on Web usage mining, especially on the understanding of a session and its reconstruction. In order to analyze this new browsing behavior, we introduce a generic browsing model extending the traditional serial or single window model to cover the use of multiple tabs. Based on this model, we present and analyze an approach to detect use of multiple tabs within sessions. The existence and increasing prominence of the use of multiple tabs is shown by this approach to be of relevance to business analysis as well as research results

Guidance Performance Indicator " Web Metrics for Information Driven Web Sites

For the evaluation of Web sites, a multitude of metrics are available. Apart from general statistical measures, success metrics reflect the degree to which a Web site achieves its defined objectives. Particularly metrics for e-commerce sites based on transaction analysis are commonly available and well understood. In contrast to transaction based sites, the success of Web sites geared toward information delivery is harder to quantify since there is no direct feedback of user intent. User feedback is only directly available on transactional Web sites. We introduce a metric to measure the success of an information driven Web site in meeting its objective to deliver the desired information in a timely and usable fashion. We propose to assign a value to each click based on the type of transition, duration and semantic distance. These values are then combined into a scoring model describing the success of a Web site in meeting its objectives. The resulting metric is introduced as the GPI and its applicability shown on a large corporate Web site.

Tracking Topic Evolution in News Environments

For companies acting on a global scale, the necessity to monitor and analyze news channels and consumer-generated media on the Web, such as weblogs and n news-groups, is steadily increasing. In particular the identification of novel trends and upcoming issues, as well as their dynamic evolution over time, is of utter importance to corporate communications and market analysts. Automated machine learning systems using clustering techniques have only partially succeeded in addressing these newly arising requirements, failing in their endeavor to properly assign short-term hype topics to long-term trends. We propose an approach which allows to monitor news wire on different levels of temporal granularity, extracting key-phrases that reflect short-term topics as well as longer-term trends by means of statistical language modelling. Moreover, our approach allows for assigning those windows of smaller scope to those of longer intervals.

Mining and Exploring Unstructured Customer Feedback Data Using Language Models and Treemap Visualizations

We propose an approach for exploring large corpora of textual customer feedback in a guided fashion, bringing order to massive amounts of unstructured information. The prototypical system we implemented allows an analyst to assess labelled clusters in a graphical fashion, based on treemaps, and perform drill-down operations to investigate the topic of interest in a more fine-grained manner. Labels are chosen by simple but effective term weighting schemes and lay the foundations for assigning feedback postings to clusters. In order to allow for drill-down operations leading to new clusters of refined information, we present an approach that contrasts foreground and background models of feedback texts when stepping into the currently selected set of feedback messages. The prototype we present is already in use at various Siemens units and has been embraced by marketing analysts.

Using Topic Discovery to Segment Large Communication Graphs for Social Network Analysis

The application of social network analysis to graphs found in the World Wide Web and the Internet has received increasing attention in recent years. Networks as diverse as those generated by e-mail communication, instant messaging, link structure in the Internet as well as citation and collaboration networks have all been treated with this method. So far these analyses solely utilize graph structure. There is, however, another source of information available in messaging corpora, namely content. We propose to apply the field of content analysis to the process of social network analysis. By extracting relevant and cohesive sub-networks from massive graphs, we obtain information on the actors contained in such sub-networks to a much firmer degree than before.

Discovering Communities of Interest in a Tagged On-Line Environment

Tagging and social networks have come into increasing use in concert with the rise of collaborative and interactive on-line media. The focus of tagging is herein twofold: First of all the plain annotation of existing data by a governing instance in order to increase the semantic content of unstructured data, and secondly the application of such meta-information by a community or a group of like minded users. The information contained in such social tagging reflects the point of view and understanding of the community, presenting a valuable source of information for the discovery of community structure,content and intent. This paper proposes an approach aimed at the use of community based tagging to address problems in link prediction and the discovery of complex user groups in a fleeting and unstructured web-based environment. The ideas presented in this paper are applied to a real world scenario, and the results show a distinct opportunity in community detection and support. This result will be incorporated into emerging knowledge management systems within Siemens AG in the near future.

Searchstrings revealing user intent: a better understanding of user perception

The evaluation of information driven websites by analysis of serverside available data is the objective of our approach. In our former work we developed techniques for evaluation of non-transactional websites by regarding the authors intentions and using only based on implicit user feedback. In several case studies we got aware that in single cases unsatisfied users had been evaluated positively. This divergence could be explained by not having considered the users intentions. We propose in this approach to integrate search queries within referrer informaiton as freely available information about the users intentions. By integrating this new source of information into our meta model of website structure, content and author intention, we enhance the formerly developed web success metric GPI. We apply well understood techniques such as PLSA for text categorization. Based on the latent semantic we construct a new indicator evaluating the website with respect to the user intention. By ranking all webpages with respect to the user intention manifested in the search query, we acchieve an individualized measure to evaluate a session by the users initial intention. In contrast to manual assignments of weights by the website author, our proposed measure is purely calculated allowing a generic assessment of websites without manual intervention.In a case study we can show, that this indicator evaluates the quality and usability of a website more accurately by taking the users goals under consideration. We can also show, that the initially mentioned diverging user sessions, can now be assessed according to the users perception.Due to limited information on the host side, without direct access to the client side, still some assumptions remain to be made.

Improving semantic consistency of web sites by quantifying user intent

The design and organization of a website reflects the authors intent. Since user perception and understanding of websites may differ from the authors, we propose a means to identify and quantify this difference in perception. In our approach we extract perceived semantic focus by analyzing user behavior in conjunction with keyword similarity. By combining usage and content data we identify user groups with regard to the subject of the pages they visited. Our real world data shows that these user groups are nicely distinguishable by their content focus. By introducing a distance measure of keyword coincidence between web pages and user groups, we can identify pages of similar perceived interest. A discrepancy between perceived distance and link distance in the web graph indicates an inconsistency in the web sites design. Determining usage similarity allows the web site author to optimize the content to the users needs.

Distilling Informative Content from HTML News Pages

Not only the Web abounds of information overload, but also its component molecules, the Web documents contained therein. In particular HTML news pages have become aggregates of cornucopian information blocks, such as advertisements, link lists, disclaimers and terms of use, or comments from readers. Thus, only a small fraction of all textual content appears dedicated to the actual news article itself. The amalgamation of relevant content with page clutter poses considerable concerns to applications that make use of such news information, such as search engines. We present an approach geared towards dissecting relevant from irrelevant textual content in an automated fashion. Our system extracts linguistic and structural features from merged text segments and applies various classifiers thereafter. We have conducted empirical analyses in order to compare our approachs classification performance with a human gold standard as well as two benchmark systems.

Mining and Exploring Customer Feedback Using Language Models and Treemaps

We propose an approach for exploring large corpora of textual customer feedback in a guided fashion, bringing order to massive amounts of unstructured information. The prototypical system we implemented allows an analyst to assess labelled clusters in a graphical fashion, based on treemap visualization techniques, and perform drill-down operations in order to investigate the topic of interest in a more fine-grained manner.