Michael G. Noll

Web search personalization via social bookmarking and tagging

In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study.

Telling experts from spammers: expertise ranking in folksonomies

With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.

Exploring social annotations for web document classification

Social annotation via so-called collaborative tagging describes the process by which many users add metadata in the form of unstructured keywords to shared content. In this paper, we explore and study social annotations and tagging with regard to their usefulness for web document classification by an analysis of large sets of real-world data. We are interested in finding out which kinds of documents are annotated more by end users than others, how users tend to annotate these documents, and in particular how this user-generated folk-sonomy compares with a top-down taxonomy maintained by classification experts for the same set of documents. We describe what can be deduced from the results for further research and development in the areas of document classification and information retrieval.

Authors vs. readers: a comparative study of document metadata and content in the www

Collaborative tagging describes the process by which many users add metadata in the form of unstructured keywords to shared content. The recent practical success of web services with such a tagging component like Flickr or del.icio.us has provided a plethora of user-supplied metadata about web content for everyone to leverage. In this paper, we conduct a quantitative and qualitative analysis of metadata and information provided by the authors and publishers of web documents compared with metadata supplied by end users for the same content. Our study is based on a random sample of 100,000 web documents from the Open Directory, for which we examined the original documents from the World Wide Web in addition to data retrieved from the social bookmarking service del.icio.us, the content rating system ICRA, and the search engine Google. To the best of our knowledge, this is the first study to compare user tags with the metadata and actual content of documents in the WWW on a larger scale and to integrate document popularity information in the observations. The data set of our experiments is freely available for research.

The Metadata Triumvirate: Social Annotations, Anchor Texts and Search Queries

In this paper, we study and compare three different but related types of metadata about Web documents: social annotations provided by readers of Web documents, hyperlink anchor text provided by authors of Web documents, and search queries of users trying to find Web documents. We introduce a large research data set called CABS120k, which we have created for this study from a variety of information sources such as AOL500k, the Open Directory Project, del.icio.us/Yahoo!, Google and the WWW in general. We use this data set to investigate several characteristics of said metadata including length, novelty, diversity, and similarity and discuss theoretical and practical implications.

SPEAR: SPAMMING-RESISTANT EXPERTISE ANALYSIS AND RANKING IN COLLABORATIVE TAGGING SYSTEMS

In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high‐quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph‐based algorithm, SPEAR (spamming‐resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext‐induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.

Measuring Expertise in Online Communities

Online communities have become important venues where Web users interact with each other and share their favorite items. Websites let users organize their favorite items online and benefit from one anothers collections. This article discusses the notions of experts and expertise in the context of online communities.

Design and Anatomy of a Social Web Filtering Service

An apparatus and method for sensing touch between a compression mold and a workpiece located in the compression mold including a mold cavity and a mold closure movable relative to the workpiece. The apparatus may include at least one touch sensor pad positionable to signal touch between the mold closure and the workpiece. The touch sensor pad may be in communication with a touch sensor monitor for indicating touch between the workpiece and the mold closure. The touch sensor pad may also be embodied in a touch sensor assembly.

Building a Scalable Collaborative Web Filter with Free and Open Source Software

In this case study, we describe the design and architecture of a scalable collaborative web filtering service, TaggyBear, which is powered by free and open source software. We will introduce the reader to the ideas and concepts behind TaggyBear, and discuss why we picked the software components that form the basis of the service. We will talk about how we combined or extended their functionality to build the TaggyBear service, and provide some initial benchmarking results and performance figures.