Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model

Modern search engine result pages often provide immediate value to users and organize information in such a way that it is easy to navigate. The core ranking function contributes to this and so do result snippets, smart organization of result blocks and extensive use of one-box answers or side panels. While they are useful to the user and help search engines to stand out, such features present two big challenges for evaluation. First, the presence of such elements on a search engine result page (SERP) may lead to the absence of clicks, which is, however, not related to dissatisfaction, so-called "good abandonments." Second, the non-linear layout and visual difference of SERP items may lead to non-trivial patterns of user attention, which is not captured by existing evaluation metrics. In this paper we propose a model of user behavior on a SERP that jointly captures click behavior, user attention and satisfaction, the CAS model, and demonstrate that it gives more accurate predictions of user actions and self-reported satisfaction than existing models based on clicks alone. We use the CAS model to build a novel evaluation metric that can be applied to non-linear SERP layouts and that can account for the utility that users obtain directly on a SERP. We demonstrate that this metric shows better agreement with user-reported satisfaction than conventional evaluation metrics.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 201

Description and application of the correlation between gaze and hand for the different hand events occurring during interaction with tablets

People’s activities naturally involve the coordination of gaze and hand. Research in Human-Computer Interaction (HCI) endeavours to enable users to exploit this multimodality for enhanced interaction. With the abundance of touch screen devices, direct manipulation of an interface has become a dominating interaction technique. Although touch enabled devices are prolific in both public and private spaces, interactions with these devices do not fully utilise the benefits from the correlation between gaze and hand. Touch enabled devices do not employ the richness of the continuous manual activity above their display surface for interaction and a lot of information expressed by users through their hand movements is ignored. This thesis aims at investigating the correlation between gaze and hand during natural interaction with touch enabled devices to address these issues. To do so, we set three objectives. Firstly, we seek to describe the correlation between gaze and hand in order to understand how they operate together: what is the spatial and temporal relationship between these modalities when users interact with touch enabled devices? Secondly, we want to know the role of some of the inherent factors brought by the interaction with touch enabled devices on the correlation between gaze and hand, because identifying what modulates the correlation is crucial to design more efficient applications: what are the impacts of the individual differences, the task characteristics and the features of the on-screen targets? Thirdly, as we want to see whether additional information related to the user can be extracted from the correlation between gaze and hand, we investigate the latter for the detection of users’ cognitive state while they interact with touch enabled devices: can the correlation reveal the users’ hesitation? To meet the objectives, we devised two data collections for gaze and hand. In the first data collection, we cover the manual interaction on-screen. In the second data collection, we focus instead on the manual interaction in-the-air. We dissect the correlation between gaze and hand using three common hand events users perform while interacting with touch enabled devices. These events comprise taps, stationary hand events and the motion between taps and stationary hand events. We use a tablet as a touch enabled device because of its medium size and the ease to integrate both eye and hand tracking sensors. We study the correlation between gaze and hand for tap events by collecting gaze estimation data and taps on tablet in the context of Internet related tasks, representative of typical activities executed using tablets. The correlation is described in the spatial and temporal dimensions. Individual differences and effects of the task nature and target type are also investigated. To study the correlation between gaze and hand when the hand is in a stationary situation, we conducted a data collection in the context of a Memory Game, chosen to generate enough cognitive load during playing while requiring the hand to leave the tablet’s surface. We introduce and evaluate three detection algorithms, inspired by eye tracking, based on the analogy between gaze and hand patterns. Afterwards, spatial comparisons between gaze and hands are analysed to describe the correlation. We study the effects on the task difficulty and how the hesitation of the participants influences the correlation. Since there is no certain way of knowing when a participant hesitates, we approximate the hesitation with the failure of matching a pair of already seen tiles. We study the correlation between gaze and hand during hand motion between taps and stationary hand events from the same data collection context than the case mentioned above. We first align gaze and hand data in time and report the correlation coefficients in both X and Y axis. After considering the general case, we examine the impact of the different factors implicated in the context: participants, task difficulty, duration and type of the hand motion. Our results show that the correlation between gaze and hand, throughout the interaction, is stronger in the horizontal dimension of the tablet rather than in its vertical dimension, and that it varies widely across users, especially spatially. We also confirm the eyes lead the hand for target acquisition. Moreover, we find out that the correlation between gaze and hand when the hand is in the air above the tablet’s surface depends on where the users look at on the tablet. As well, we show that the correlation during eye and hand during stationary hand events can indicate the users’ indecision, and that while the hand is moving, the correlation depends on different factors, such as the degree of difficulty of the task performed on the tablet and the nature of the event before/after the motion

Are all the frames equally important?

In this work, we address the problem of measuring and predicting temporal video saliency - a metric which defines the importance of a video frame for human attention. Unlike the conventional spatial saliency which defines the location of the salient regions within a frame (as it is done for still images), temporal saliency considers importance of a frame as a whole and may not exist apart from context. The proposed interface is an interactive cursor-based algorithm for collecting experimental data about temporal saliency. We collect the first human responses and perform their analysis. As a result, we show that qualitatively, the produced scores have very explicit meaning of the semantic changes in a frame, while quantitatively being highly correlated between all the observers. Apart from that, we show that the proposed tool can simultaneously collect fixations similar to the ones produced by eye-tracker in a more affordable way. Further, this approach may be used for creation of first temporal saliency datasets which will allow training computational predictive algorithms. The proposed interface does not rely on any special equipment, which allows to run it remotely and cover a wide audience.Comment: CHI'20 Late Breaking Work

Gaze–mouse coordinated movements and dependency with coordination demands in tracing.

Eye movements have been shown to lead hand movements in tracing tasks where subjects have to move their fingers along a predefined trace. The question remained, whether the leading relationship was similar when tracing with a pointing device, such as a mouse; more importantly, whether tasks that required more or less gaze–mouse coordination would introduce variation in this pattern of behaviour, in terms of both spatial and temporal leading of gaze position to mouse movement. A three-level gaze–mouse coordination demand paradigm was developed to address these questions. A substantial dataset of 1350 trials was collected and analysed. The linear correlation of gaze–mouse movements, the statistical distribution of the lead time, as well as the lead distance between gaze and mouse cursor positions were all considered, and we proposed a new method to quantify lead time in gaze–mouse coordination. The results supported and extended previous empirical findings that gaze often led mouse movements. We found that the gaze–mouse coordination demands of the task were positively correlated to the gaze lead, both spatially and temporally. However, the mouse movements were synchronised with or led gaze in the simple straight line condition, which demanded the least gaze–mouse coordination

Attention and information acquisition: Comparison of mouse-click with eye-movement attention tracking

Attention is crucial as a fundamental prerequisite for perception. The measurement of attention in viewing and recognizing the images that surround us constitutes an important part of eye movement research, particularly in advertising-effectiveness research. Recording eye and gaze (i.e. eye and head) movements is considered the standard procedure for measuring attention. However, alternative measurement methods have been developed in recent years, one of which is mouse-click attention tracking (mcAT) by means of an on-line based procedure that measures gaze motion via a mouse-click (i.e. a hand and finger positioning maneuver) on a computer screen.Here we compared the validity of mcAT with eye movement attention tracking (emAT). We recorded data in a between subject design via emAT and mcAT and analyzed and compared 20 subjects for correlations. The test stimuli consisted of 64 images that were assigned to eight categories. Our main results demonstrated a highly significant correlation (p<0.001) between mcAT and emAT data. We also found significant differences in correlations between different image categories. For simply structured pictures of humans or animals in particular, mcAT provided highly valid and more consistent results compared to emAT. We concluded that mcAT is a suitable method for measuring the attention we give to the images that surround us, such as photographs, graphics, art or digital and print advertisements

Stacking-based visualization of trajectory attribute data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well

Enriching Verbal Feedback from Usability Testing: Automatic Linking of Thinking-Aloud Recordings and Stimulus using Eye Tracking and Mouse Data

The think aloud method is an important and commonly used tool for usability optimization. However, analyzing think aloud data could be time consuming. In this paper, we put forth an automatic analysis of verbal protocols and test the link between spoken feedback and the stimulus using eye tracking and mouse tracking. The gained data - user feedback linked to a specific area of the stimulus - could be used to let an expert review the feedback on specific web page elements or to visualize on which parts of the web page the feedback was given. Specifically, we test if participants fixate on or point with the mouse to the content of the webpage that they are verbalizing. During the testing, participants were shown three websites and asked to verbally give their opinion. The verbal responses, along with the eye and cursor movements were recorded. We compared the hit rate, defined as the percentage of verbally mentioned areas of interest (AOIs) that were fixated with gaze or pointed to with the mouse. The results revealed a significantly higher hit rate for the gaze compared to the mouse data. Further investigation revealed that, while the mouse was mostly used passively to scroll, the gaze was often directed towards relevant AOIs, thus establishing a strong association between spoken words and stimuli. Therefore, eye tracking data possibly provides more detailed information and more valuable insights about the verbalizations compared to the mouse data

Quantifying gaze and mouse interactions on spatial visual interfaces with a new movement analytics methodology

This research was supported by the Royal Society International Exchange Programme (grant no. IE120643).Eye movements provide insights into what people pay attention to, and therefore are commonly included in a variety of human-computer interaction studies. Eye movement recording devices (eye trackers) produce gaze trajectories, that is, sequences of gaze location on the screen. Despite recent technological developments that enabled more affordable hardware, gaze data are still costly and time consuming to collect, therefore some propose using mouse movements instead. These are easy to collect automatically and on a large scale. If and how these two movement types are linked, however, is less clear and highly debated. We address this problem in two ways. First, we introduce a new movement analytics methodology to quantify the level of dynamic interaction between the gaze and the mouse pointer on the screen. Our method uses volumetric representation of movement, the space-time densities, which allows us to calculate interaction levels between two physically different types of movement. We describe the method and compare the results with existing dynamic interaction methods from movement ecology. The sensitivity to method parameters is evaluated on simulated trajectories where we can control interaction levels. Second, we perform an experiment with eye and mouse tracking to generate real data with real levels of interaction, to apply and test our new methodology on a real case. Further, as our experiment tasks mimics route-tracing when using a map, it is more than a data collection exercise and it simultaneously allows us to investigate the actual connection between the eye and the mouse. We find that there seem to be natural coupling when eyes are not under conscious control, but that this coupling breaks down when instructed to move them intentionally. Based on these observations, we tentatively suggest that for natural tracing tasks, mouse tracking could potentially provide similar information as eye-tracking and therefore be used as a proxy for attention. However, more research is needed to confirm this.Publisher PDFPeer reviewe

Factors influencing visual attention switch in multi-display user interfaces: a survey

Multi-display User Interfaces (MDUIs) enable people to take advantage of the different characteristics of different display categories. For example, combining mobile and large displays within the same system enables users to interact with user interface elements locally while simultaneously having a large display space to show data. Although there is a large potential gain in performance and comfort, there is at least one main drawback that can override the benefits of MDUIs: the visual and physical separation between displays requires that users perform visual attention switches between displays. In this paper, we present a survey and analysis of existing data and classifications to identify factors that can affect visual attention switch in MDUIs. Our analysis and taxonomy bring attention to the often ignored implications of visual attention switch and collect existing evidence to facilitate research and implementation of effective MDUIs.Postprin