Application and evaluation of multi-dimensional diversity

Traditional information retrieval (IR) systems mostly focus on finding documents relevant to queries without considering other documents in the search results. This approach works quite well in general cases; however, this also means that the set of returned documents in a result list can be very similar to each other. This can be an undesired system property from a user's perspective. The creation of IR systems that support the search result diversification present many challenges, indeed current evaluation measures and methodologies are still unclear with regards to specific search domains and dimensions of diversity. In this paper, we highlight various issues in relation to image search diversification for the ImageClef 2009 collection and tasks. Furthermore, we discuss the problem of defining clusters/subtopics by mixing diversity dimensions regardless of which dimension is important in relation to information need or circumstances. We also introduce possible applications and evaluation metrics for diversity based retrieval

Implicit search trails for video recommendation

In this demo paper we demonstrate our approach and system for using implicit actions involved in video search to provide recommendations to users. The goal of this system is to improve the quality of the results that users find, and in doing so, help users to explore a large and difficult information space and help them consider search options that they may not have considered otherwise. Results of a user evaluation show that this approach achieves all of these goals

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Search trails using user feedback to improve video search

In this paper we present an innovative approach for aiding users in the difficult task of video search. We use community based feedback mined from the interactions of previous users of our video search system to aid users in their search tasks. This feedback is the basis for providing recommendations to users of our video retrieval system. The ultimate goal of this system is to improve the quality of the results that users find, and in doing so, help users to explore a large and difficult information space and help them consider search options that they may not have considered otherwise. In particular we wish to make the difficult task of search for video much easier for users. The results of a user evaluation indicate that we achieved our goals, the performance of the users in retrieving relevant videos improved, and users were able to explore the collection to a greater extent

Collaborative search trails for video search

In this paper we present an approach for supporting users in the difficult task of searching for video. We use collaborative feedback mined from the interactions of earlier users of a video search system to help users in their current search tasks. Our objective is to improve the quality of the results that users find, and in doing so also assist users to explore a large and complex information space. It is hoped that this will lead to them considering search options that they may not have considered otherwise. We performed a user centred evaluation. The results of our evaluation indicate that we achieved our goals, the performance of the users in finding relevant video clips was enhanced with our system; users were able to explore the collection of video clips more and users demonstrated a preference for our system that provided recommendations

The University of Glasgow at ImageClefPhoto 2009

In this paper we describe the approaches adopted to generate the five runs submitted to ImageClefPhoto 2009 by the University of Glasgow. The aim of our methods is to exploit document diversity in the rankings. All our runs used text statistics extracted from the captions associated to each image in the collection, except one run which combines the textual statistics with visual features extracted from the provided images. The results suggest that our methods based on text captions significantly improve the performance of the respective baselines, while the approach that combines visual features with text statistics shows lower levels of improvements

A comparison of artificial driving sounds for automated vehicles

As automated vehicles currently do not provide sufficient feedback relating to the primary driving task, drivers have no assurance that an automated vehicle has understood and can cope with upcoming traffic situations [16]. To address this we conducted two user evaluations to investigate auditory displays in automated vehicles using different types of sound cues related to the primary driving sounds: acceleration, deceleration/braking, gear changing and indicating. Our first study compared earcons, speech and auditory icons with existing vehicle sounds. Our findings suggested that earcons were an effective alternative to existing vehicle sounds for presenting information related to the primary driving task. Based on these findings a second study was conducted to further investigate earcons modulated by different sonic parameters to present primary driving sounds. We discovered that earcons containing naturally mapped sonic parameters such as pitch and timbre were as effective as existing sounds in a simulated automated vehicle

Video test collection with graded relevance assessments

Relevance is a complex, but core, concept within the field of Information Retrieval. In order to allow system comparisons the many factors that influence relevance are often discarded to allow abstraction to a single score relating to relevance. This means that a great wealth of information is often discarded. In this paper we outline the creation of a video test collection with graded relevance assessments, to the best of our knowledge the first example of such a test collection for video retrieval. To directly address the shortcoming above we also gathered behavioural and perceptual data from assessors during the assessment process. All of this information along with judgements are available for download. Our intention is to allow other researchers to supplement the judgements to help create an adaptive test collection which contains supplementary information rather than a completely static collection with binary judgements

Exploring how drivers perceive spatial earcons in automated vehicles

Automated vehicles seek to relieve the human driver from primary driving tasks, but this substantially diminishes the connection between driver and vehicle compared to manual operation. At present, automated vehicles lack any form of continual, appropriate feedback to re-establish this connection and offer a feeling of control. We suggest that auditory feedback can be used to support the driver in this context. A preliminary field study that explored how drivers respond to existing auditory feedback in manual vehicles was first undertaken. We then designed a set of abstract, synthesised sounds presented spatially around the driver, known as Spatial Earcons, that represented different primary driving sounds e.g. acceleration. To evaluate their effectiveness, we undertook a driving simulator study in an outdoor setting using a real vehicle. Spatial Earcons performed as well as Existing Vehicle Sounds during automated and manual driving scenarios. Subjective responses suggested Spatial Earcons produced an engaging driving experience. This paper argues that entirely new synthesised primary driving sounds, such as Spatial Earcons, can be designed for automated vehicles to replace Existing Vehicle Sounds. This creates new possibilities for presenting primary driving information in automated vehicles using auditory feedback, in order to re-establish a connection between driver and vehicle