A two-step approach for interest estimation from gaze behavior in digital catalog browsing

While eye gaze data contain promising clues for inferring the interests of viewers of digital catalog content, viewers often dynamically switch their focus of attention. As a result, a direct application of conventional behavior analysis techniques, such as topic models, tends to be affected by items or attributes of little or no interest to the viewer. To overcome this limitation, we need to identify “when” the user compares items and to detect “which attribute types/values” reflect the user’s interest. This paper proposes a novel two-step approach to addressing these needs. Specifically, we introduce a likelihood-based short-term analysis method as the first step of the approach to simultaneously determine comparison phases of browsing and detect the attributes on which the viewer focuses, even when the attributes cannot be directly obtained from gaze points. Using probabilistic latent semantic analysis, we show that this short-term analysis step greatly improves the results of the subsequent step. The effectiveness of the framework is demonstrated in terms of the capability to extract combinations of attributes relevant to the viewer’s interest, which we call aspects, and also to estimate the interest described by these aspects

A probabilistic approach for eye-tracking based process tracing in catalog browsing

Eye movements are an important cue to understand consumer decision processes. Findings from existing studies suggest that the consumer decision process consists of a few different browsing states such as screening and evaluation. This study proposes a hidden Markov-based gaze model to reveal the characteristics and temporal changes of browsing states in catalog browsing situations. Unlike previous models that employ a heuristic rule-based approach, our model learns the browsing states in a bottom-up manner. Our model employs information about how often a decision maker looks at a selected item (the item finally selected by a decision maker) to identify the browsing states. We evaluated our model using eye tracking data in digital catalog browsing and confirmed our model can split decision process into meaningful browsing states. Finally, we propose an estimation method of browsing states that does not require the information of the selected item for applications such as an interactive decision support

Evaluation campaigns and TRECVid

The TREC Video Retrieval Evaluation (TRECVid) is an international benchmarking activity to encourage research in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video corpus, automatic detection of a variety of semantic and low-level video features, shot boundary detection and the detection of story boundaries in broadcast TV news. This paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, highlighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation benchmarking campaign and this allows us to discuss whether such campaigns are a good thing or a bad thing. There are arguments for and against these campaigns and we present some of them in the paper concluding that on balance they have had a very positive impact on research progress

Mediating disruption in human-computer interaction from implicit metrics of attention

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 143-150).Multitasking environments cause people to be interrupted constantly, often disrupting their ongoing activities and impeding reaching their goals. This thesis presents a disruption reducing approach designed to support the user's goals and optimize productivity that is based on a model of the user's receptivity to an interruption. The model uses knowledge of the interruption content, context and priority of the task(s) in progress, user actions and goal-related concepts to mediate interruptions. The disruption management model is distinct from previous work by the addition of implicit sensors that deduce the interruption content and user context to help determine when an interruption will disrupt an ongoing activity. Domain-independent implicit sensors include mouse and keyboard behaviors, and goal-related concepts extracted from the user documents. The model also identifies the contextual relationship between interruptions and user goals as an important factor in how interruptions are controlled. The degree to which interruptions are related to the user goal determines how those interruptions will be received. We tested and evolved the model in various cases and showed significant improvement in both productivity and satisfaction. A disruption manager application controls interruptions on common desktop computing activities, such as web browsing and instant messaging. The disruption manager demonstrates that mediating interruptions by supporting the user goals can improve performance and overall productivity. Our evaluation shows an improvement in success of over 25% across prioritization conditions for real life computing environments.(cont.) Goal priority and interruption relevance play an important role in the interruption decision process and several experiments these factors on people's reactions and availability to interruptions, and overall performance. These experiments demonstrate that people recognize the potential benefits of being interrupted and adjust their susceptibility to interruptions during highly prioritized tasks. The outcome of this research includes a usable model that can be extended to tasks as diverse as driving an automobile and performing computer tasks. This thesis supports mediating technologies that will recognize the value of communication and control interruptions so that people are able to maintain concentration amidst their increasingly busy lifestyles.by Ernesto Arroyo Acosta.Ph.D

Creating sparks: comparing search results using discriminatory search term word co-occurrence to facilitate serendipity in the enterprise.

Categories or tags that appear in faceted search interfaces which are representative of an information item, rarely convey unexpected or non-obvious associated concepts buried within search results. No prior research has been identified which assesses the usefulness of discriminative search term word co-occurrence to generate facets to act as catalysts to facilitate insightful and serendipitous encounters during exploratory search. In this study, 53 scientists from two organisations interacted with semi-interactive stimuli, 74% expressing a large/moderate desire to use such techniques within their workplace. Preferences were shown for certain algorithms and colour coding. Insightful and serendipitous encounters were identified. These techniques appear to offer a significant improvement over existing approaches used within the study organisations, providing further evidence that insightful and serendipitous encounters can be facilitated in the search user interface. This research has implications for organisational learning, knowledge discovery and exploratory search interface design

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

Ensembles of choice-based models for recommender systems

In this thesis, we focused on three main paradigms: Recommender Systems, Decision Making, and Ensembles. The work is structured as follows. First, the thesis analyzes the potential of choice-based models. The motivation behind this was based on the idea of applying sound decisionmaking paradigms, such as choice and utility theory, in the field of Recommender Systems. Second, this research analyzes the cognitive process underlying choice behavior. On the one hand, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. On the other hand, cognitive were fitted using rational, emotional, and attentional features. Finally, the work explores the hybridization of choice-based models with ensembles. The goal is to take the best of the two worlds: transparency and performance. Two main methods were analyzed to build optimal choice-based ensembles: uninformed and informed. First one, two strategies were evaluated: 1-Learner and N-Learners ensembles. Second one, we relied on three types of prior information: (1) High diversity, (2) Low error prediction (MSE), (3) and Low crowd error