The use of implicit evidence for relevance feedback in web retrieval

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field

eBay users form stable groups of common interest

Market segmentation of an online auction site is studied by analyzing the users' bidding behavior. The distribution of user activity is investigated and a network of bidders connected by common interest in individual articles is constructed. The network's cluster structure corresponds to the main user groups according to common interest, exhibiting hierarchy and overlap. Key feature of the analysis is its independence of any similarity measure between the articles offered on eBay, as such a measure would only introduce bias in the analysis. Results are compared to null models based on random networks and clusters are validated and interpreted using the taxonomic classifications of eBay categories. We find clear-cut and coherent interest profiles for the bidders in each cluster. The interest profiles of bidder groups are compared to the classification of articles actually bought by these users during the time span 6-9 months after the initial grouping. The interest profiles discovered remain stable, indicating typical interest profiles in society. Our results show how network theory can be applied successfully to problems of market segmentation and sociological milieu studies with sparse, high dimensional data.Comment: Major revision of the manuscript. Methodological improvements and inclusion of analysis of temporal development of user interests. 19 pages, 12 figures, 5 table

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

The future of enterprise groupware applications

This paper provides a review of groupware technology and products. The purpose of this review is to investigate the appropriateness of current groupware technology as the basis for future enterprise systems and evaluate its role in realising, the currently emerging, Virtual Enterprise model for business organisation. It also identifies in which way current technological phenomena will transform groupware technology and will drive the development of the enterprise systems of the future

Recipe Recommendation: Accuracy and Reasoning

Abstract. Food and diet are complex domains for recommender tech-nology but the need for systems which assist users in embarking on and engaging with healthy living programs has never been more real. With the obesity epidemic reaching new levels each day many practitioners are looking to ICT for novel and effective ways to engage and sustain engage-ment with online solutions. Here we report on a large scale analysis of real user ratings on a large set of recipes in order to judge the applicability and practicality of each. We use traditional content based, collaborative filtering and machine learning algorithms and discuss the trends in the data which reflect user reasoning and decision making strategies

Intra-amniotic delivery of CFTR-expressing adenovirus does not reverse cystic fibrosis phenotype in inbred CFTR-knockout mice

This article is available open access through the publisher’s website at the link below. Copyright © 2008 The American Society of Gene Therapy.Due to its early onset and severe prognosis, cystic fibrosis (CF) has been suggested as a candidate disease for in utero gene therapy. In 1997, a study was published claiming that to how transient prenatal expression of CF transmembrane conductance regulator (CFTR) from an in utero –injected adenovirus vector could achieve permanent reversal of the CF intestinal pathology in adult CF knockout mice, despite the loss of CFTR transgene expression by birth. This would imply that the underlying cause of CF is a prenatal defect for which lifelong cure can be achieved by transient prenatal expression of CFTR. Despite criticism at the time of publication, no independent verification of this contentious finding has been published so far. This is vital for the development of future therapeutic strategies as it may determine whether CF gene therapy should be performed prenatally or postnatally. We therefore reinvestigated this finding with an identical adenoviral vector and a knockout CF mouse line (CftrtmlCam) with a completely inbred genetic background to eliminate any effects due to genetic variation. After delivery of the CFTR-expressing adenovirus to the fetal mouse, both vector DNA and transgenic CFTR expression were detected in treated animals postpartum but statistically no significant difference in survival was observed between the Cftr–/– mice treated with the CFTR-adenovirus and those treated with the control vector.Sport Aiding Medical Research for Kids, the Cystic Fibrosis Trust, and the Katharine Dormandy Trust

BIIL 284 reduces neutrophil numbers but increases P. aeruginosa bacteremia and inflammation in mouse lungs

Background: A clinical study to investigate the leukotriene B4 (LTB4)-receptor antagonist BIIL 284 in cystic fibrosis (CF) patients was prematurely terminated due to a significantly increased risk of adverse pulmonary events. We aimed to establish the effect of BIIL284 in models of Pseudomonas aeruginosa lung infection, thereby contributing to a better understanding of what could have led to adverse pulmonary events in CF patients. Methods: P. aeruginosa DNA in the blood of CF patients during and after acute pulmonary exacerbations and in stable patients with non-CF bronchiectasis (NCFB) and healthy individuals was assessed by PCR. The effect of BIIL 284 treatment was tested in an agar bead murine model of P. aeruginosa lung infection. Bacterial count and inflammation were evaluated in lung and other organs. Results: Most CF patients (98%) and all patients with NCFB and healthy individuals had negative P. aeruginosa DNA in their blood. Similarly, the P. aeruginosa-infected mice showed bacterial counts in the lung but not in the blood or spleen. BIIL 284 treatment decreased pulmonary neutrophils and increased P. aeruginosa numbers in mouse lungs leading to significantly higher bacteremia rates and lung inflammation compared to placebo treated animals. Conclusions: Decreased airway neutrophils induced lung proliferation and severe bacteremia in a murine model of P. aeruginosa lung infection. These data suggest that caution should be taken when administering anti-inflammatory compounds to patients with bacterial infections

Empirical analysis of web-based user-object bipartite networks

Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This Letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative clustering coefficient, to quantify the clustering behavior based on the collaborative selection. Accordingly, the clustering properties and clustering-degree correlations are investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.Comment: 6 pages, 7 figures and 1 tabl