Evaluating Advanced Search Interfaces using Established Information-Seeking Models

When users have poorly defined or complex goals, keyword searching may not provide sufficient support. Subsequently, more advanced systems are being developed to support richer modes of search. This paper presents a formative framework for evaluating advancing search systems, which are providing more versatile environments that become increasingly hard to compare. This is done by quantifying their strengths and weaknesses in supporting user tactics and varying user conditions. This framework combines established models of users, user needs, and user behaviours to achieve this. The framework is applied to evaluate three advanced search interfaces and shows promising results

A study of factors affecting the utility of implicit relevance feedback

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF

Stochastic Privacy

Online services such as web search and e-commerce applications typically rely on the collection of data about users, including details of their activities on the web. Such personal data is used to enhance the quality of service via personalization of content and to maximize revenues via better targeting of advertisements and deeper engagement of users on sites. To date, service providers have largely followed the approach of either requiring or requesting consent for opting-in to share their data. Users may be willing to share private information in return for better quality of service or for incentives, or in return for assurances about the nature and extend of the logging of data. We introduce \emph{stochastic privacy}, a new approach to privacy centering on a simple concept: A guarantee is provided to users about the upper-bound on the probability that their personal data will be used. Such a probability, which we refer to as \emph{privacy risk}, can be assessed by users as a preference or communicated as a policy by a service provider. Service providers can work to personalize and to optimize revenues in accordance with preferences about privacy risk. We present procedures, proofs, and an overall system for maximizing the quality of services, while respecting bounds on allowable or communicated privacy risk. We demonstrate the methodology with a case study and evaluation of the procedures applied to web search personalization. We show how we can achieve near-optimal utility of accessing information with provable guarantees on the probability of sharing data

Evaluating advanced search interfaces using established information-seeking model

When users have poorly defined or complex goals search interfaces offering only keyword searching facilities provide inadequate support to help them reach their information-seeking objectives. The emergence of interfaces with more advanced capabilities such as faceted browsing and result clustering can go some way to some way toward addressing such problems. The evaluation of these interfaces, however, is challenging since they generally offer diverse and versatile search environments that introduce overwhelming amounts of independent variables to user studies; choosing the interface object as the only independent variable in a study would reveal very little about why one design out-performs another. Nonetheless if we could effectively compare these interfaces we would have a way to determine which was best for a given scenario and begin to learn why. In this article we present a formative framework for the evaluation of advanced search interfaces through the quantification of the strengths and weaknesses of the interfaces in supporting user tactics and varying user conditions. This framework combines established models of users, user needs, and user behaviours to achieve this. The framework is applied to evaluate three search interfaces and demonstrates the potential value of this approach to interactive IR evaluation

An Ecological Framework to Assess Sustainability Impacts for an Evolving Consumer Electronic Product System

Consumer electronics have revolutionized the manner in which we work, read, and entertain ourselves. However, this transformation comes at a high cost, with significant energy input and emissions releases across all stages of the electronic product life cycle. The limited success of per product efficiency improvements, often formulated in the field of industrial ecology, does not address the electronic product system as a whole because escalating consumption may actually offset any individual impact reductions. Additionally, existing industrial ecology models fail to effectively capture energy, material, and waste flows associated with real consumption patterns, as consumers purchase, use, and discard a group of interrelated devices such as desktops, laptops, printers, mobile phones, and digital cameras. To address this challenge, this dissertation develops and applies novel industrial ecology methodologies to more effectively characterize changes to rapidly evolving and interrelated product systems. Notably, these approaches borrow heavily from underutilized biological ecology concepts from community ecology and optimal foraging theory, but adapted for use as applied to a complex product system like consumer electronics. These approaches can lead to more effective design, production, green purchasing decisions, and end of life practices and policies, while at the same time expand industrial ecology\u27s traditional focus on the ecosystem metaphor and ‘per product’ approaches and strengthen its connection to the source science: biological ecological roots