Mood modeling: accuracy depends on active logging and reflection

Current behavior change systems often demand extremely advanced sensemaking skills, requiring users to interpret personal datasets in order to understand and change behavior. We describe EmotiCal, a system to help people better manage their emotions, that finesses such complex sensemaking by directly recommending specific mood-boosting behaviors to users. This paper first describes how we develop the accurate mood models that underlie these mood-boosting recommendations. We go on to analyze what types of information contribute most to the predictive power of such models, and how we might design systems to reliably collect such predictive information. Our results show that we can derive very accurate mood models with relatively small samples of just 70 users. These models explain 61% of variance by combining: (a) user reflection about the effects of different activities on mood, (b) user explanations of how different activities affect mood, and (c) individual differences. We discuss the implications of these findings for the design of behavior change systems, as well as for theory and practice. Contrary to many recent approaches, our findings argue for the importance of active user reflection rather than passive sensing.     

WallTop: Managing Overflowing Windows on a Large Display

With the ever increasing amount of digital information, users desire more screen real estate to process daily desktop computing work, and might well benefit from using a large high-resolution display for information management. Unfortunately, we know very little about users' behaviors when using such a display for daily computing, and current user interfaces are mainly designed for normal-sized desktop monitors, which might not well suit a large display. In this article, we first present a longitudinal study that investigates how users manage overflowing digital information on a wall-sized display in a personal desktop computing context by comparing it with single and dual desktop monitors. Results showed users' unanimous preferences of working on a large display and revealed large-display users' unique activity patterns of managing windows. Guided by the study results, we designed a set of interaction techniques that provide greater flexibility in managing multiple windows. They include facile methods for selecting, moving, and resizing multiple windows using an active window boundary called Fringe, rearranging selected windows using multi- and single-window marking menus, packing/unpacking the selected windows using easily activated icons, and freely adjusting the order of overlapping windows with a Jab-to-Lift operation. We coherently integrated these techniques with traditional operations in a large-display window management prototype called WallTop. Two rounds of usability testing showed that users can quickly and easily learn the new interaction techniques and apply them to realistic window management tasks on a large display with increased efficiency.     

Performance and usability tradeoff in a cluster display wall

Cluster-based display walls provide cost-effective and scalable display infrastructures with high resolution and large display area, making them suitable for a wide range of high-resolution applications. As a consequence, a wide offer of new cluster display-wall platforms together with their software frameworks have been proposed. Their performance and the satisfaction of their users have aroused the interest of some researchers. This work is focused on the Liquid Galaxy cluster display wall originally built to run Google Earth to create an immersive experience for the users. In this paper, the Liquid Galaxy is benchmarked running Google Earth, as a representative interactive application with high performance requirements, in different configurations and environments, to test the satisfaction, effectiveness and efficiency. Thus, we wish to know how users react to the system performance. In order to do so, we use a performance metric defined in previous research to relate the performance of the system with the user’s perception. Taking into account the trend of this metric in the experimentation, we model the behavior of the system in a way that the performance for any given visualization cluster running Google Earth could be predicted by using a reference system.     

WalkMap: Developing an augmented reality map application for wearable computers

We have designed, implemented, and evaluated a map application for wearable computer users. Our application, called WalkMap, is targeted at a walking user in an urban environment, offering the user both navigational aids as well as contextual information. WalkMap uses augmented reality techniques to display a map on the surrounding area on the user's head-worn display. WalkMap is constructed by the means of software development, user interface design and evaluations, and existing knowledge on how humans use maps and navigate. The key design driver in our approach is intuitivity of use. In this paper, we present the design and implementation process of our application, considering human-map interfaces, technical implementation, and human-computer interfaces. We identify some of the key issues in these areas, and present the way they have been solved. We also present some usability evaluation results.     

Users joining multiple sites: Friendship and popularity variations across sites

Our social media experience is no longer limited to a single site. We use different social media sites for different purposes and our information on each site is often partial. By collecting complementary information for the same individual across sites, one can better profile users. These profiles can help improve online services such as advertising or recommendation across sites. To combine complementary information across sites, it is critical to understand how information for the same individual varies across sites. In this study, we aim to understand how two fundamental properties of users vary across social media sites. First, we study how user friendship behavior varies across sites. Our findings show how friend distributions for individuals change as they join new sites. Next, we analyze how user popularity changes across sites as individuals join different sites. We evaluate our findings and demonstrate how our findings can be employed to predict how popular users are likely to be on new sites they join.     

Characterizing online health and wellness information consumption: A study

To seek answers to health queries, we often find ourselves on a quest to assimilate information from varied online sources. This information search and fusion from different sources elicits user preferences, which can be driven by demographics, context, and socio-economic factors. To that end, we study these factors as part of health-information seeking behavior of users on a large health and wellness-based knowledge sharing online platform. We begin by identifying the topical interests of users from different content consumption sources. Using these topical preferences, we explore information consumption and health-seeking behavior across three contextual dimensions: user-based demographic attributes, time-related features, and community-based socioeconomic factors. We then study how these context signals can be used to explain specific user health topic preferences. Our findings suggest that linking demographic features to user profiles is more effective in explaining health preferences than other features. Our work demonstrates the value of using contextual factors to characterize and understand the content consumption of users seeking health and wellness information online.     

High-resolution gaming: Interfaces,notifications, and the user experience

Advances in technology and display hardware have allowed the resolution of monitors – and video games – to incrementally improve over the past three decades. However, little research has been done in preparation for the resolutions that will be available in the future if this trend continues. We developed a number of display prototypes to explore the different aspects of gaming on large, high-resolution displays.By running a series of experiments, we were not only able to evaluate the benefits of these displays for gaming, but also identify potential user interface and hardware issues that can arise. Building on these results, various interface designs were developed to better notify the user of passive and critical game information as well as to overcome difficulties with mouse-based interaction on these displays. Different display form factors and user input devices are also explored in order to determine how they can further enhance the gaming experience. In many cases, the new techniques can be applied to single-monitor games and solve the same problems in real-world, high-resolution applications.     

Exploring touch feedback display of virtual keyboards for reduced eye movements

When typing on smartphones or palm tablets, users generally make an effort to type correctly while simultaneously checking the small keyboard and the text display. Unlike physical keyboards that allow users to perform typing based on long-term muscle memory, virtual keyboards typically require more frequent eye movements between the keyboard and the text display areas.This study proposes a new way of designing a virtual keyboard display to reduce the effort associated with frequent eye-movements. For this study, we developed virtual keyboard display systems featuring both static and dynamic word-by-word (WBW) feedback displays. The two display systems were examined in comparison with a more conventional method known as character-by-character (CBC) feedback display. We investigated user satisfaction, typing performance and the user’s eye gaze shifts. Eye gaze shifts were measured between the keyboard and the text display areas across the three conditions using self-report, log, and eye-tracking measures. In the static WBW condition, the words being typed displayed in a fixed area at the top of the virtual keyboard; in the dynamic WBW display, the words displayed in a small popup window at the tip of the selected key.Using a repeated measure experiment for the three display conditions, participants were asked to type fifteen phrases using a palm tablet while wearing eye-tracking glasses for each condition. We conducted a mixed-model design ANOVA with group (SLOW vs. FAST typing; men vs. women) as between-subject factors and display condition (CBC vs. WBW). We found a significant (11%) improvement in typing speed with the dynamic WBW over the CBC display for less experienced keyboard users. In addition, participants reported higher satisfaction with the two WBW conditions than the CBC condition. Eye fixations, dwell times, and heat map data also supported that WBW displays are advantageous for less experienced, slower typists by helping them stay focused more on the keyboard, thus reducing eye transitions to the text display. Our study systematically demonstrates how and to what extent the virtual keyboard display strategy influences typing performance and subjective experience based on self-reports and eye-tracking measures. The approach and findings of this study should provide useful information and practical guidance to mobile application developers and designers who are interested in improving virtual keyboard functionalities and user satisfaction.     

基于Web集群的海量影像显示技术研究

针对目前Web显示技术只允许单个用户通过远程视频会议和桌面镜像与单个异地用户协同工作,不能满足多用户海量信息共享显示的要求,不支持海量影像超高分辨显示需要等问题.本文根据多用户海量信息共享显示的特点以及显示的难点,研究了基于Web集群海量影像多异地用户在拼接显示屏上超高分辨显示等技术.实验结果表明,提出的Web集群并行拼接海量影像显示技术是有效的,能够同时为多个异地用户的海量影像在拼接显示屏上超高分辨显示提供可行的解决方案.     

Towards accurate cursorless pointing: the effects of ocular dominance and handedness

Pointing gestures are our natural way of referencing distant objects and thus widely used in HCI for controlling devices. Due to current pointing models’ inherent inaccuracies, most of the systems using pointing gestures so far rely on visual feedback showing users where they point at. However, in many environments, e.g., smart homes, it is rarely possible to display cursors since most devices do not contain a display. Therefore, we raise the question of how to facilitate accurate pointing-based interaction in a cursorless context. In this paper we present two user studies showing that previous cursorless techniques are rather inaccurate as they lack important considerations about users’ characteristics that would help in minimizing inaccuracy. We show that pointing accuracy could be significantly improved by acknowledging users’ handedness and ocular dominance. In a first user study (n=?33), we reveal the large effect of ocular dominance and handedness on human pointing behavior. Current ray-casting techniques neglect both ocular dominance and handedness as effects onto pointing behavior, precluding them from accurate cursorless selection. With a second user study (n=?25), we show that accounting for ocular dominance and handedness yields to significantly more accurate selections compared to two previously published ray-casting techniques. This speaks for the importance of considering users’ characteristics further to develop better selection techniques to foster more robust accurate selections.     

面向用户互联网访问日志的异常点击分析

随着互联网用户人数的日益增长,用户行为分析已经成为互联网技术领域重要的研究方法之一。在日志中去除异常点击,对于准确挖掘用户行为的意图和习惯十分重要。该文采用某公司提供的真实用户互联网访问日志,对日志中的连续点击,单IP多用户以及单用户多IP等可能的异常点击,从访问集中度,用户平均访问量等方面进行了分析。我们认为对于连续点击,用户行为分析研究人员可以分情况滤去多余点击或该用户所有点击,而对于单IP多用户和单用户多 IP的点击,我们建议不做处理。     

Assisting people with visual impairments in aiming at a target on a large wall-mounted display

Large interactive displays have become ubiquitous in our everyday lives, but these displays are designed for the needs of sighted people. In this paper, we specifically address assisting people with visual impairments to aim at a target on a large wall-mounted display. We introduce a novel haptic device, which explores the use of vibrotactile feedback in blind user search strategies on a large wall-mounted display. Using mid-air gestures aided by vibrotactile feedback, we compared three target-aiming techniques: Random (baseline) and two novel techniques – Cruciform and Radial. The results of our two experiments show that visually impaired participants can find a target significantly faster with the Cruciform and Radial techniques than with the Random technique. In addition, they can retrieve information on a large display about twice as fast by augmenting speech feedback with haptic feedback in using the Radial technique. Although a large number of studies have been done on assistive interfaces for people who have visual impairments, very few studies have been done on large vertical display applications for them. In a broader sense, this work will be a stepping-stone for further research on interactive large public display technologies for users who are visually impaired.     

Evaluating recommender systems from the user’s perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users?? perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS??s ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users?? adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system??s recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system??s overall perceptive qualities and how these qualities influence users?? behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.     

基于Period-Near算法的用户移动位置预测

新浪微博是一种允许大量用户彼此分享包括位置在内的个人信息的电子媒介,它使得掌握用户的运动轨迹成为可能。尽管用户的运动和移动模式有着高度的自由性和多样性,但是周期性的运动是非常频繁的现象,因此寻找用户的周期行为对于了解用户的动作至关重要。在本文中将这个问题定义为“预测用户将要去哪里”,该问题涉及2个子问题：如何发现用户的历史行为以及如何应用用户的历史行为来预测其将来的行为。假设用户的行为是周期性的,并且如果用户在一个位置的时间足够长,那么他/她将会一直待在这个位置。基于这2个假设,提出一个4阶段算法Period-Near来解决这个问题。在算法的第1阶段挖掘用户的周期性行为,第2阶段发现其较为频繁的移动,第3阶段了解用户在最近一段时间所处的位置,第4阶段是根据前3个阶段来预测用户接下来将要去哪里。无论是在综合数据上还是实际数据上的实验研究均表明本文方法具有一定的有效性。     

垂直学习社区基于学习兴趣与风格的社会化推荐算法

垂直学习社区包含了海量的学习资源,出现了信息过载现象,个性化推荐是解决这个难题的方法之一.但垂直学习社区中评分数据稀疏而文本、社交信息丰富,传统的协同过滤推荐算法不完全适用.基于用户产生的文本和行为信息,利用作者主题模型构建新的用户学习兴趣相似度衡量模型;根据用户交互行为信息综合考虑信任与不信任因素构建用户全面信任关系计算全面信任度;通过分析用户多维度学习行为模式,自动识别用户学习风格;最后提出融合兴趣相似度、全面信任度及学习风格的社会化推荐算法.用垂直学习社区网站CSDN实际数据集进行了实验分析.结果表明本文提出的推荐方法能更好向用户推荐其感兴趣的学习资源,有效地提高了推荐精度,进而提高用户学习效果.     

Reciprocal Recommender Systems: Analysis of state-of-art literature,challenges and opportunities towards social recommendation

There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the user’s preferences, needs and/or behavior. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end user’s reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the “matching” recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.     

移动端非显式用户身份信息的隐私问题研究

智能设备给人们带来方便的同时也记录了大量使用者的使用习惯、位置、访问内容等隐私信息.文章综合考虑用户行为数据的采集方式,数据处理方式以及存储方式,自主设计用户行为数据的采集系统,并在智能终端设备上采集用户的大量非显式身份信息数据,包括网络流量信息、屏幕状态信息等,通过对这些数据进行处理和分析,发现利用这些非显式的用户身份信息可以有效对用户身份进行识别,并能推测出部分用户间的社会关系.实验表明,基于非显式身份信息数据的研究对保护用户隐私有重要的现实意义和很大的应用价值.     

Effects of User’s Hand Orientation and Spatial Movements on Free Hand Interactions with Large Displays

In motion-sensing interaction with large displays through bare hands, we can observe that users alternate their hands and move their bodies frequently. What cause such actions and how these actions affect free hand interaction results are less systematically investigated. To address this gap in knowledge, we conducted studies on Pointer-Acceleration (PA)-based free hand interactions of target selection and found that (1) users made more frequent hand alternations when selecting small targets with large movement amplitudes, as in such cases users were not only affected by observable arm fatigue, but were also motivated to switch hands for higher selection accuracy and convenience; (2) hand alternation led to the hand orientation effects: target selection on display areas at the operating hand’s side was more efficient and accurate than that at the opposite side; (3) large movement amplitudes on the user interface increased users’ physical movements in front of the large display, which harmed selection efficiency; (4) selection of small targets led to a closer interaction distance, while large movement amplitudes led to a larger interaction distance; and (5) selection results were affected by interaction distances, as users gained high efficiency, but low accuracy at a large distance and low efficiency, but high accuracy at a close distance. Given these results, this article discusses practical implications for applying PA-based free hand interaction techniques and designing related user interfaces on large displays.     

Understanding the mechanism of social tie in the propagation process of social network with communication channel

The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers have proposed a series of propagation models, generally, which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works, so that the existing models failed to clearly describe the process a user be activated by a neighbor. To this end, in this paper, we analyze the close correspondence between social tie in propagation process and communication channel, thus we propose to exploit the communication channel to describe the information propagation process between users, and design a social tie channel (STC) model. The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference, and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.     

Understanding and designing for intermediated information tasks in India

It's important to consider both primary and secondary users when designing for intermediated interaction scenarios in India and elsewhere in the developing world. However, most of this research has focused on supporting users in the developed world who are voluntarily collaborating on a computing task. Many users in India, especially those from disadvantaged classes, have only partial or no physical access to computing devices. We refer to these users as secondary users to distinguish them from the primary users that the interface design process traditionally considers. Secondary users must interact with information resources via a proxy primary user who has the required access rights and skills. The proxy's filtering and funneling decisions limit the secondary users' information-seeking behavior; the secondary user might also have an unequal power relationship with the proxy. Therefore, secondary users might never know the full scope of actions and knowledge available to them. If we are to realize the egalitarian potential of computing, we must consider secondary users in the design process. We must develop technologies that recognize the needs and aspirations of all classes of users, including those without direct access to the user interface. In fact, by designing user interfaces explicitly supporting intermediated tasks, both primary and secondary users can benefit.