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.

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 folksonomy 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.

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 CABS120k08 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.

In this paper 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. Firstly, 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. Secondly, 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 much better than the original HITS algorithm and simple statistical measures currently used in most collaborative tagging systems.