Improving organic recollection with memory prosthetics

The limitations of human memory are well documented [ERJ07]. As exposure to information continues to increase, information-based memory problems will become even more commonplace and systems will be needed to support peoples’ organic memory (OM). A potential solution, that has been a popular research focus in recent years, is to create prosthetic memory devices (PM) –digital stores containing all of an individual’s information, media content and context data [GBL+02]. The idea is to create a resource where the user can lookup forgotten information if and when the need arises. There are obvious trade-offs between the permanence and reliability of PM against the speed of access that can be achieved with OM [KW07] and consequently, most research in this area has aimed at improving facilities to search within these stores e.g. [KJ10]. While there is obvious merit in this approach, one problem is that as search facilities improve and costs of re-finding decrease, it is possible that users will become overly reliant on PM with detrimental effects on OM, comparable to the effects of continued calculator use on mental arithmetic abilities. Our work will complement re-finding research by investigating how PM content can be used to facilitate improved organic recollection, reducing the need to re-find. In this position statement, we outline the fundamental approach and present some key open research questions to be addressed by our research

Towards task-based personal information management evaluations

Personal Information Management (PIM) is a rapidly growing area of research concerned with how people store, manage and re-find information. A feature of PIM research is that many systems have been designed to assist users manage and re-find information, but very few have been evaluated.This has been noted by several scholars and explained by the difficulties involved in performing PIM evaluations.The difficulties include that people re-find information from within unique personal collections; researchers know little about the tasks that cause people to re-find information; and numerous privacy issues concerning personal information. In this paper we aim to facilitate PIM evaluations by addressing each of these difficulties. In the first part, we present a diary study of information re-finding tasks. The study examines the kind of tasks that require users to re-find information and produces a taxonomy of re-finding tasks for email messages and web pages. In the second part, we propose a task-based evaluation methodology based on our findings and examine the feasibility of the approach using two different methods of task creation

Developing bulletin board visualizations

Prevalent text-based representations within online communities and bulletin boards often make it difficult to contextualise the activity and interactions that are taking place. Visualisation techniques have been successfully applied in a range of domains to augment and enhance existing communication technologies such as bulletin boards. This paper presents a new approach to visualising bulletin board activity: BulB. The use of BulB visualisations enables users to gain an overall picture of bulletin board activity and develop an understanding of the overall interaction context

Workshop on Desktop Search

The first SIGIR workshop on Desktop Search was held on 23rd July 2010 in Geneva, Switzerland. The workshop consisted of 2 industrial keynotes, 10 paper presentations in a combination of oral and poster format and several discussion sessions. This report presents an overview of the scope and contents of the workshop and outlines the major outcomes

An evaluation of recommendation algorithms for online recipe portals

Better models of food preferences are required to realise the oft touted potential of food recommenders to aid with the obesity crisis. Many of the food recommender evaluations in the literature have been performed with small convenience samples, which limits our conidence in the generalisability of the results. In this work we test a range of collaborative iltering (CF) and content-based (CB) recommenders on a large dataset crawled from the web consisting of naturalistic user interaction data over a 15 year period. The results reveal strengths and limitations of diferent approaches. While CF approaches consistently outperform CB approaches when testing on the complete dataset, our experiments show that to improve on CF methods require a large number of users (> 637 when sampling randomly). Moreover the results show diferent facets of recipe content to ofer utility. In particular one of the strongest content related features was a measure of health derived from guidelines from the UK Food Safety Agency. This inding underlines the challenges we face as a community to develop recommender algorithms, which improve the healthfulness of the food people choose to eat.publishedVersio

BulB - visualizing bulletin board activity

Visualisation is well known as an effective means of enriching user interaction with complex systems. Recent research with online communities has considered the application of visualisation tool support, with the intention of further improving communication between community members. This paper reviews existing work in this area with specific reference to the application of visualisation to improve user interaction within online forums such as bulletin boards. The paper then outlines work undertaken by the authors to develop a second-generation visualisation tool - 'BulB'