The benefits of opening recommendation to human interaction

This paper describes work in progress that uses an interactive recommendation process to construct new objects which are tailored to user preferences. The novelty in our work is moving from the recommendation of static objects like consumer goods, movies or books, towards dynamically-constructed recommendations which are built as part of the recommendation process. As a proof-of-concept we build running or jogging routes for visitors to a city, recommending routes to users according to their preferences and we present details of this system

An examination of user-focused context-gathering techniques in recommendation interfaces

Attempts to capture context within applications take a wide variety of forms. While it is generally accepted that a user’s current context shapes how they perceive and interact with a system such as a recommender we here explore a novel method of interacting with the user to gain a conceptual understanding of their own frame of reference. By drawing on a more human-centric approach we show that users accept and participate in sharing of context readily as part of an interactive system

Social contextuality and conversational recommender systems

As people continue to become more involved in both creating and consuming information, new interactive methods of retrieval are being developed. In this thesis we examine conversational approaches to recommendation, that is, the act of suggesting items to users based on the system’s understanding of them. Conversational recommendation is a recent contribution to the task of information discovery. We propose a novel approach to conversation around recommendation, examining how it is improved to work with collaborative filtering, a common recommendation algorithm. In developing new ways to recommend information to people we also examine their methods of information seeking, exploring the role of conversational recommendation, using both interview and sensed brain signals. We also look at the implications of the wealth of social and sensed information now available and how it improves the task of accurate recommendation. By allowing systems to better understand the connections between users and how their social impact can be tracked we show improved recommendation accuracy. We look at the social information around recommendations, proposing a directed influence approach between socially connected individuals, for the purpose of weighting recommendations with the wisdom of influencers. We then look at the semantic relationships that might seem to indicate wisdom (i.e. authors on a book-ranking site) to see if the ``wisdom of the few'' can be traced back to those conventionally considered wise in the area. Finally we look at ``contextuality'' (the ability of sets of contextual sensors to accurately recommend items across groups of people) in recommendation, showing that different users have very different uses for context within recommendation. This thesis shows that conversational recommendation can be generalised to work well with collaborative filtering, that social influence contributes to recommendation accuracy, and that contextual factors should not be treated the same for each user

Remote monitoring of landfill gases from solid waste landfill

Landfill gas is primarily made up from Methane (CH4) and Carbon Dioxide (CO2). Global methane emissions from landfill are estimated to be between 30 and 70 million tonnes each year. Methane makes landfill gas explosive when it is present in the 5-15% concentration range. Landfill directives state that licensed landfills in the UK and Ireland should never exceed a concentration of 1% for CH4 and 1.5% for CO2, at perimeter borehole wells[1]. However, the EPA has cited large noncompliance with suggested targets [2]. This is partly due to the single point nature of the CH4 and CO2 sampling, and also the low sampling frequency. This research group has developed a dual autonomous CH4 and CO2 sensor, and has successfully run extensive field trials over the last 2 years. Currently using the system, three live data streams are being populated logging methane and carbon dioxide values in real-time on three different landfill sites

An examination of user-focused context-gathering techniques in recommendation interfaces

Attempts to capture context within applications take a wide variety of forms. While it is generally accepted that a user’s current context shapes how they perceive and interact with a system such as a recommender we here explore a novel method of interacting with the user to gain a conceptual understanding of their own frame of reference. By drawing on a more human-centric approach we show that users accept and participate in sharing of context readily as part of an interactive system

Remote monitoring of landfill gases from solid waste landfill (including real time data integration to a web based data portal)

The broad reaching implications of global warming throughout the world have placed the spotlight on gas sensing within the scientific and industrialised communities. Monitoring of greenhouse gas emissions is crucial for the management of potential risks to the environment and indeed to human health. It is widely accepted that a majority of these emissions are caused by human activities including the burning of fossil fuels. Less appreciated is the amount of emissions (methane and carbon dioxide) emitted from landfill sites despite ongoing efforts from the EU and EPA to regulate such emissions. Our efforts over the past 2 years have resulted in fully autonomous landfill gas analysers capable of performing analysis of gas samples for CO2 and CH4 at remote locations with a frequency of 4 measurements per day. Subsequently, the data is delivered in real time to a live sensor portal page where users can view key gas concentration levels from any web-enabled browser. The end-to-end nature of this sensing approach has allowed governing authorities to easily determine potential risks when compared to the current manual approach of sampling sites in rough terrain with an expensive hand held instrument. Moreover, the modularity of this design allows scope for easy adaptation to quantitatively measure other key sensing targets in multiple environments