Creative professional users musical relevance criteria

Although known item searching for music can be dealt with by searching metadata using existing text search techniques, human subjectivity and variability within the music itself make it very difficult to search for unknown items. This paper examines these problems within the context of text retrieval and music information retrieval. The focus is on ascertaining a relationship between music relevance criteria and those relating to relevance judgements in text retrieval. A data-rich collection of relevance judgements by creative professionals searching for unknown musical items to accompany moving images using real world queries is analysed. The participants in our observations are found to take a socio-cognitive approach and use a range of content and context based criteria. These criteria correlate strongly with those arising from previous text retrieval studies despite the many differences between music and text in their actual content

Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs

According to the principle of polyrepresentation, retrieval accuracy may improve through the combination of multiple and diverse information object representations about e.g. the context of the user, the information sought, or the retrieval system. Recently, the principle of polyrepresentation was mathematically expressed using subjective logic, where the potential suitability of each representation for improving retrieval performance was formalised through degrees of belief and uncertainty. No experimental evidence or practical application has so far validated this model. We extend the work of Lioma et al. (2010), by providing a practical application and analysis of the model. We show how to map the abstract notions of belief and uncertainty to real-life evidence drawn from a retrieval dataset. We also show how to estimate two different types of polyrepresentation assuming either (a) independence or (b) dependence between the information objects that are combined. We focus on the polyrepresentation of different types of context relating to user information needs (i.e. work task, user background knowledge, ideal answer) and show that the subjective logic model can predict their optimal combination prior and independently to the retrieval process

Towards Social Information Seeking and Interaction on the Web

User generated content is one of the key concepts of the social web (a. k. a “Web 2.0”) and enables users to search and interact with information that has been created (e.g. blogs) or annotated by other users (e.g. in tagging systems). Consequently, information seeking and interaction have been extended by a social dimension. The interaction can be social in so far that user generated content is searched and retrieved or, in a more direct manner that social interactions are carried out before, during or after search by communicating through Web 2.0 features like (micro-)blog posts, comments, and ratings. This paper focuses on social interactions during the search process by combining a model introduced by Shneiderman (2002) which attempts to describe human motivation for collaboratively using computers with an explorative model for social search by Evans and Chi (2008)

The role of places and spaces in lifelog retrieval

Finding relevant interesting items when searching or browsing within a large multi-modal personal lifelog archive is a significant challenge. The use of contextual cues to filter the collection and aid in the determination of relevant content is often suggested as means to address such challenges. This work presents an exploration of the various locations, garnered through context logging, several participants engaged in during personal information access over a 15 month period. We investigate the implications of the varying data accessed across multiple locations for context-based retrieval from such collections. Our analysis highlights that a large number of spaces and places may be used for information access, but high volume of content is accessed in few

Integrating memory context into personal information re-finding

Personal information archives are emerging as a new challenge for information retrieval (IR) techniques. The user’s memory plays a greater role in retrieval from person archives than from other more traditional types of information collection (e.g. the Web), due to the large overlap of its content and individual human memory of the captured material. This paper presents a new analysis on IR of personal archives from a cognitive perspective. Some existing work on personal information management (PIM) has begun to employ human memory features into their IR systems. In our work we seek to go further, we assume that for IR in PIM system terms can be weighted not only by traditional IR methods, but also taking the user’s recall reliability into account. We aim to develop algorithms that combine factors from both the system side and the user side to achieve more effective searching. In this paper, we discuss possible applications of human memory theories for this algorithm, and present results from a pilot study and a proposed model of data structure for the HDMs achieves

Social and interactional practices for disseminating current awareness information in an organisational setting.

Current awareness services are designed to keep users informed about recent developments based around user need profiles. In organisational settings, they may operate through both electronic and social interactions aimed at delivering information that is relevant, pertinent and current. Understanding these interactions can reveal the tensions in current awareness dissemination and help inform ways of making services more effective and efficient. We report an in-depth, observational study of electronic current awareness use within a large London law firm. The study found that selection, re-aggregation and forwarding of information by multiple actors gives rise to a complex sociotechnical distribution network. Knowledge management staff act as a layer of “intelligent filters” sensitive to complex, local information needs; their distribution decisions address multiple situational relevance factors in a situation fraught with information overload and restrictive time-pressures. Their decisions aim to optimise conflicting constraints of recall, precision and information quantity. Critical to this is the use of dynamic profile updates which propagate back through the network through formal and informal social interactions. This supports changes to situational relevance judgements and so allows the network to ‘self-tune’. These findings lead to design requirements, including that systems should support rapid assessment of information items against an individual’s interests; that it should be possible to organise information for different subsequent uses; and that there should be back-propagation from information consumers to providers, to tune the understanding of their information needs

Augmenting human memory using personal lifelogs

Memory is a key human facility to support life activities, including social interactions, life management and problem solving. Unfortunately, our memory is not perfect. Normal individuals will have occasional memory problems which can be frustrating, while those with memory impairments can often experience a greatly reduced quality of life. Augmenting memory has the potential to make normal individuals more effective, and those with significant memory problems to have a higher general quality of life. Current technologies are now making it possible to automatically capture and store daily life experiences over an extended period, potentially even over a lifetime. This type of data collection, often referred to as a personal life log (PLL), can include data such as continuously captured pictures or videos from a first person perspective, scanned copies of archival material such as books, electronic documents read or created, and emails and SMS messages sent and received, along with context data of time of capture and access and location via GPS sensors. PLLs offer the potential for memory augmentation. Existing work on PLLs has focused on the technologies of data capture and retrieval, but little work has been done to explore how these captured data and retrieval techniques can be applied to actual use by normal people in supporting their memory. In this paper, we explore the needs for augmenting human memory from normal people based on the psychology literature on mechanisms about memory problems, and discuss the possible functions that PLLs can provide to support these memory augmentation needs. Based on this, we also suggest guidelines for data for capture, retrieval needs and computer-based interface design. Finally we introduce our work-in-process prototype PLL search system in the iCLIPS project to give an example of augmenting human memory with PLLs and computer based interfaces