Jörg Schlötterer

Towards a feature-rich data set for personalized access to long-tail content

Personalized data access has become one of the core challenges for intelligent information access, especially for nonmainstream long-tail content, as can be found in digital libraries. One of the main reasons that personalization remains a difficult task is the lack of standardized test corpora. In this paper we provide a comprehensive analysis of feature requirements for personalization together with a data collection tool for generating user models and collecting data for personalization of search and recommender system optimization in the long-tail. Based on the feature analysis, we provide a feature-rich publicly available data set, covering web content consumption and creation tasks. Our data set contains user models for eight users, including performed tasks, relevant topics for each task, relevance ratings, and relations between focus text and search queries. Altogether, the data set consists of 217 tasks, 4562 queries and over 15.000 ratings. On this data we perform automatic query prediction from web page content, achieving an accuracy of 89% using term identity, capitalization and part-of-speech tags as features. The results of the feature analysis can serve as guideline for feature collection for long-tail content personalization, and the provided data set as a gold standard for learning and evaluation of user models as well as for optimizing recommender or search engines for long-tail domains.

Ubiquitous Access to Digital Cultural Heritage

The digitization initiatives in the past decades have led to a tremendous increase in digitized objects in the cultural heritage domain. Although digitally available, these objects are often not easily accessible for interested users because of the distributed allocation of the content in different repositories and the variety in data structure and standards. When users search for cultural content, they first need to identify the specific repository and then need to know how to search within this platform (e.g., usage of specific vocabulary). The goal of the EEXCESS project is to design and implement an infrastructure that enables ubiquitous access to digital cultural heritage content. Cultural content should be made available in the channels that users habitually visit and be tailored to their current context without the need to manually search multiple portals or content repositories. To realize this goal, open-source software components and services have been developed that can either be used as an integrated infrastructure or as modular components suitable to be integrated in other products and services. The EEXCESS modules and components comprise (i) Web-based context detection, (ii) information retrieval-based, federated content aggregation, (iii) metadata definition and mapping, and (iv) a component responsible for privacy preservation. Various applications have been realized based on these components that bring cultural content to the user in content consumption and content creation scenarios. For example, content consumption is realized by a browser extension generating automatic search queries from the current page context and the focus paragraph and presenting related results aggregated from different data providers. A Google Docs add-on allows retrieval of relevant content aggregated from multiple data providers while collaboratively writing a document. These relevant resources then can be included in the current document either as citation, an image, or a link (with preview) without having to leave disrupt the current writing task for an explicit search in various content providers’ portals.

Supporting web surfers in finding related material in digital library repositories

Web surfers often face the need for additional information beyond the page they are currently reading. While such related material is available in digital library repositories, finding it within these repositories can be a challenging task. In order to ease the burden for the user, we present an approach to construct queries automatically from a textual paragraph. Named entities from the paragraph and a query scheme, which includes the topic of the paragraph form the two pillars of this approach, which is applicable to any search system, that supports keyword queries. Evaluation results point towards users not being able to find optimal queries and needing support in doing so.

Focus Paragraph Detection for Online Zero-Effort Queries: Lessons learned from Eye-Tracking Data

In order to realize zero-effort retrieval in a web-context, it is crucial to identify the part of the web page the user is focusing on. In this paper, we investigate the identification of focus paragraphs in web pages. Starting from a naive baseline for paragraph and focus paragraph detection, we conducted an eye-tracking study to evaluate the most promising features. We found that single features (mouse position, paragraph position, mouse activity) are less predictive for gaze which confirms findings from other studies. The results indicate that an algorithm for focus paragraph detection needs to incorporate a weighted combination of those features as well as additional features, e.g. semantic context derived from the users web history.

From context to query

Providers of scientific, cultural and educational resources face the problem that their content largely remains untapped in the so called long tail of the Web. Just-in-time retrieval is a viable way to increase the visibility of this long tail content. It has been shown to increase the amount of information viewed, by proactively retrieving information, based on a users context and presenting results in an unobtrusive manner [6]. The main motivation for our work lies in the question on how to apply just-in-time information retrieval on long tail content in a web environment.

QueryCrumbs: A Compact Visualization for Navigating the Search Query History

Models of human information seeking reveal that search, in particular ad-hoc retrieval, is non-linear and iterative. Despite these findings, todays search user interfaces do not support non-linear navigation, like for example backtracking in time. In this work, we propose QueryCrumbs, a compact and easy-to-understand visualization for navigating the search query history supporting iterative query refinement. We apply a multi-layered interface design to support novices and firsttime users as well as intermediate users. The formative evaluation with first-time and intermediate users showed that the interactions can be easily performed, and the visual encodings were well understood without instructions. Results indicate that QueryCrumbs can support users when searching for information in an iterative manner.