Web page annotation and adaptive navigation support are two active, but independent research directions focused on the same goal: expanding the functionality of the Web as a hypertext system. The goal of the AnnotatEd system presented in this paper has been to integrate annotation and adaptive navigation support into a single value-added service where the components can reinforce each other and create new unique attributes. This paper describes the implementation of AnnotatEd from early prototypes to the current version, which has been explored in several contexts. We summarize some lessons we learned during the development process and which defined the current functionality of the system. We also present the results of several classroom studies of the system. These results demonstrate the importance of the browsing-based information access supported by AnnotatEd and the value of both the annotation and navigation support functionalities offered by the system.

We present a study of the degree to which annotations overlap when several researchers read the same set of scientific articles. Our objective is to determine whether there is sufficient evidence to suggest that information about which passages initial readers tend to annotate might be used to recommend important passages to later readers of the same material. We found that readers exhibit a high degree of overlap in the passages they annotate, that these passages account for a small but significant fraction of the total document, and that such passages are distributed throughout a document rather than concentrated in the same few sections in each paper (e.g., the results section). These findings indicate that work on developing a passage recommendation model based on annotation is warranted.

While current search engines serve known-item search such as homepage finding very well, they generally cannot support exploratory search effectively. In exploratory search, users do not know their information needs precisely and also often lack the needed knowledge to formulate effective queries, thus querying alone, as supported by the current search engines, is insufficient, and browsing into related information would be very useful. Currently, browsing is mostly done by following hyperlinks embedded on Web pages. In this paper, we propose to leverage search logs to allow a user to browse beyond hyperlinks with a multi-resolution topic map constructed based on search logs. Specifically, we treat search logs as “footprints ” left by previous users in the information space and build a multi-resolution topic map to semantically capture and organize them in multiple granularities. Such a topic map can support a user to zoom in, zoom out, and navigate horizontally over the information space, and thus provide flexible and effective browsing capabilities for end users. To test the effectiveness of the proposed methods of supporting browsing, we rely on real search logs and a commercial search engine to implement our proposed methods. Our experimental results show that the proposed topic map is effective to support browsing beyond hyperlinks.

Search engines return ranked lists of Web pages in response to queries. These pages are starting points for post-query navigation, but may be insufficient for search tasks involving multiple steps. Search trails mined from toolbar logs start with a query and contain pages visited by one user during post-query navigation. Implicit endorsements from many trails can enhance result ranking. Rather than using trails solely to improve ranking, it may also be worth providing trail information directly to users. In this paper, we quantify the benefit that users currently obtain from trailfollowing and compare different methods for finding the best trail for a given query and each top-ranked result. We compare the relevance, topic coverage, topic diversity, and utility of trails selected using different methods, and break out findings by factors such as query type and origin relevance. Our findings demonstrate value in trails, highlight interesting differences in the performance of trailfinding algorithms, and show we can find best-trails for a query that outperform the trails most users follow. Findings have implications for enhancing Web information seeking using trails.

Finding relevant information in a hyperspace has been a much studied problem for many years. With the emergence of so called Web 2.0 technologies we have seen the use of social systems for retrieval tasks increasing dramatically. Each system collects and exploits its own pool of community wisdom for the benefit of its users. In this paper we suggest a form of retrieval which exploits the pools of wisdom of multiple social technologies, specifically social search and social navigation. The paper details the added user benefits of merging several sources of social wisdom. We present details of the ASSIST engine developed to integrate social support mechanisms for the users of information repositories. The goal of this paper is to present the main features of the integrated community-based personalization engine that we have developed in order to improve retrieval in the hyperspace of information resources. It also reports the results of an empirical study of this technology.

We studied how actual users find items of interest in today’s complex, recommender-rich information environments, what role recommenders play in it, and if recommenders increase perceived social presence. We used applied ethnography, on-location observation and interviewing, and Amazon as the environment to get an accurate picture of user activity. We found that users are increasingly relying on recommenders in finding items of interest. Since they have developed strategies to combine keyword searching with recommenders for discovery, recommenders should not be developed in isolation of the whole because users do not use them in isolation. In addition, while some users feel that recommenders add to the sense of social presence, others feel that they are not enough to create a sense of others being present. 1

Abstract—Collaborative or “Social ” filtering has been successfully deployed over the years as a technique for analysing large amounts of user-preference knowledge to predict interesting items for an individual user. The black-box nature of most collaborative filtering (CF) applications leave the user wondering how the system arrived at its recommendation. In this paper we introduce PeerChooser, a collaborative recommender system with an interactive interface which provides the user not only an explanation of the recommendation process, but the opportunity to manipulate a graph of their peers at varying levels of granularity, to reflect aspects of their current requirements. PeerChooser’s prediction component reads directly from the graph to yield the same results as a benchmark recommendation algorithm. Users then improve on these predictions by tweaking the graph in various ways. PeerChooser compares favorably against the benchmark in live evaluations and equally well in automated accuracy tests. I.

The Mobile Web promises a new age of anytime, anywhere information access to billions of users across the globe. However, the Mobile Internet represents a challenging information access environment, particularly from a search standpoint. In this paper we present two visual interfaces for improved mobile search. First, we present SearchBrowser, a map-based interface that offers richer end-user interactions by taking into account important mobile contexts including location and time. Second, we consider the social context of mobile search and present SocialSearchBrowser; a proofof-concept interface that incorporates social networking capabilities to improve the search and information discovery experience of mobile subscribers. Author Keywords

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Abstract. The situations in which we search form a context: a complex set of variables describing our intentions, our personal characteristics, the data and systems available for searching, and our physical, social and organizational environments. Different contexts can mean that we want search systems to behave differently or to offer different responses. Creating search systems and search interfaces to be contextually sensitive raises many research challenges: what aspects of a searcher’s context are useful to know about, how can we model context for use by retrieval systems and how do we evaluate search systems in context? In this lecture we will look at why differences in context can affect how we want search systems to operate and ways that we can use contextual information to help search systems behave more intelligently to our changing context. We will examine some new types of system that use different types of user context to learn about users, to adapt their response to different users or to help us make better search decisions.