Using interaction data for improving the offline and online evaluation of search engines

This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.

Conceptualisation, design and development of a primary school based child healthy weight intervention

INTRODUCTION: In common with much of the developed world, Scotland has a severe and well established problem with overweight and obesity in childhood with recent figures demonstrating that 31% of Scottish children aged 2-15 years old were overweight including obese in 2014. This problem is more pronounced in socioeconomically disadvantaged groups and in older children across all economic groups (Scottish Health Survey, 2014). Children who are overweight or obese are at increased risk of a number of adverse health outcomes in the short term and throughout their life course (Lobstein and Jackson-Leach, 2006). The Scottish Government tasked all Scottish Health Boards with developing and delivering child healthy weight interventions to clinically overweight or obese children in an attempt to address this health problem. It is therefore imperative to deliver high quality, affordable, appropriately targeted interventions which can make a sustained impact on children’s lifestyles, setting them up for life as healthy weight adults. This research aimed to inform the design, readiness for application and Health Board suitability of an effective primary school-based curricular child healthy weight intervention. METHODS: the process involved in conceptualising a child healthy weight intervention, developing the intervention, planning for implementation and subsequent evaluation was guided by the PRECEDE-PROCEED Model (Green and Kreuter, 2005) and the Intervention Mapping protocol (Lloyd et al. 2011). RESULTS: The outputs from each stage of the development process were used to formulate a child healthy weight intervention conceptual model then develop plans for delivery and evaluation. DISCUSSION: The Fit for School conceptual model developed through this process has the potential to theoretically modify energy balance related behaviours associated with unhealthy weight gain in childhood. It also has the potential to be delivered at a Health Board scale within current organisational restrictions.