Understanding children's collaborative interactions in shared environments

  Traditional computer technology offers limited support for face-to-face, synchronous collaboration. Consequently, children who wish to collaborate while using computers must adapt their interactions to the single-user paradigm of most personal computers. Recent technological advances have enabled the development of co-located groupware systems offering support for concurrent, multi–user interactions around a shared display. These systems provide a unique collaboration environment in which users share both the physical and the virtual workspace. This paper examines how such technology impacts children's collaboration. Findings from this research show that when concurrent, multi–user interaction is supported on a shared display, children exhibit collaborative behaviour similar to their interactions during paper-based activities. The findings also suggest strengths and weaknesses of various mechanisms for supporting synchronous interactions that have implications for the design of computer systems to support children's face-to-face collaboration.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

协同系统中的角色及转换机制

作为拓展人类合作空间的辅助工具，CSCW(Computer Supported Cooperative Work)系统不但要利用计算机提供类似于面对面的协同环境，而且应该能够提供面对面协同环境所不能给予的工具。角色机制就是一种能够解决这个问题的好方法，它通过改变、转换处于协作活动中用户的身份和职位来更好地支持高效的协同工作。该文联系对象和角色思想, 讨论协同活动中的角色动态转换的实现。在角色机制的帮助下，用户将能够更灵活、更高效率地参与协同活动。     

Communication Behaviors in Colocated Collaborative AR Interfaces

The authors present an analysis of communication behavior in face-to-face collaboration using a multi-user augmented reality (AR) interface. 2 experiments were conducted. In the 1st experiment, collaboration with AR technology was compared with more traditional unmediated and screen-based collaboration. In the 2nd experiment, the authors compared collaboration with 3 different AR displays. Several measures were used to analyze communication behavior, and the authors found that users exhibited many of the same behaviors in a collaborative AR interface as in face-to-face unmediated collaboration. However, user communication behavior changed with the type of AR display used. The authors describe implications of these results for the design of collaborative AR interfaces and directions for future research.     

融合用户偏好和多交互网络的协同过滤算法

推荐系统中的辅助信息可以为推荐提供有用的帮助,而传统的协同过滤算法在计算用户相似度时对辅助信息的利用率低,数据稀疏性大,导致推荐的精度偏低.针对这一问题,本文提出了一种融合用户偏好和多交互网络的协同过滤算法(NIAP-CF).该算法首先根据评分矩阵和项目属性特征矩阵挖掘出用户的项目属性偏好信息,然后使用SBM方法计算用户间的项目属性偏好相似度,并用其改进用户相似度计算公式.在进行评分预测时,构建融合用户-项目属性偏好信息的多交互神经网络预测模型,使用动态权衡参数综合由用户相似度计算出的预测评分和模型的预测评分来进行项目推荐.本文使用MovieLens数据集进行实验验证,实验结果表明改进算法能够提高推荐的精度,降低评分预测的MAE和RMSE值.     

Development and evaluation of a system enhancing Second Life to support synchronous role‐based collaborative learning

Research and commercial interest toward 3D virtual worlds are recently growing because they probably represent the new direction for the next generation of web applications. Although these environments present several features that are useful for informal collaboration, structured collaboration is required to effectively use them in a working or in a didactical setting. This paper presents a system supporting synchronous collaborative learning by naturally enriching Learning Management System services with meeting management and multimedia features. Monitoring and moderation of discussions are also managed at a single group and at the teaching level. The Second Life (SL) environment has been integrated with two ad hoc developed Moodle plug‐ins and SL objects have been designed, modeled, and programmed to support synchronous role‐based collaborative activities. We also enriched SL with tools to support the capturing and displaying of textual information during collaborative sessions for successive retrieval. In addition, the multimedia support has been enhanced with functionalities for navigating multimedia contents. We also report on an empirical study aiming at evaluating the use of the proposed SL collaborative learning as compared with face‐to‐face group collaboration. Results show that the two approaches are statistically undistinguishable in terms of performance, comfort with communication, and overall satisfaction. Copyright © 2009 John Wiley & Sons, Ltd.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

Collaborative user modeling for enhanced content filtering in recommender systems

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.     

一种改进Minhash的分布式协同过滤推荐算法

协同过滤推荐算法通过研究用户的喜好,实现从海量数据资源中为用户推荐其感兴趣的内容。衡量用户（资源）的相似性是协同过滤算法的核心内容,在数据量大的系统中,用户（资源）的相似性度量会面临准确性和计算复杂性等问题,影响到推荐效果。提出一种改进的协同过滤推荐算法,提取用户兴趣偏好的多值信息,运用改进Minhash算法度量用户相似性,并结合Mapreduce分布式计算,合理、高效地产生用户邻居,实现对用户的评分推荐。实验结果表明：改进算法能有效改善大数据集的推荐准确性并提高推荐效率,降低了推荐耗时。     

An algorithm for efficient privacy-preserving item-based collaborative filtering

Collaborative filtering (CF) methods are widely adopted by existing recommender systems, which can analyze and predict user “ratings” or “preferences” of newly generated items based on user historical behaviors. However, privacy issue arises in this process as sensitive user private data are collected by the recommender server. Recently proposed privacy-preserving collaborative filtering (PPCF) methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in real online services. In this paper, an efficient privacy-preserving item-based collaborative filtering algorithm is proposed, which can protect user privacy during online recommendation process without compromising recommendation accuracy and efficiency. The proposed method is evaluated using the Netflix Prize dataset. Experimental results demonstrate that the proposed method outperforms a randomized perturbation based PPCF solution and a homomorphic encryption based PPCF solution by over 14X and 386X, respectively, in recommendation efficiency while achieving similar or even better recommendation accuracy.     

Small group decision-making in face-to-face and computer-mediated environments: the role of personality

This study investigates the potential influence of personality dimensions on the behaviour and individual processes of small group decision-making in face-to-face and computer-mediated groups. A sample of 90 students was divided into 16 groups, with between four and seven students per group. The groups were randomly assigned to start with one of two different discussion media and one of two different leaderless group discussion problems. Measures of leadership, initiative, judgement, and interpersonal sensitivity were obtained for each subject from two raters working independently using video-tapes of the face-to-face sessions and the logged interactions of the computer-mediated sessions. Further, ‘satisfaction with the process’ and ‘satisfaction with the outcome’ were assessed using a Likert-type scale. Personality was measured using the Myers Briggs Type Inventory (MBTI®). Using correlations and repeated measures ANOVAs across the outcome dimensions and the psychological type scores, the findings of this research suggest that personality plays a minor role in differentiating between the media conditions.     

A model of group satisfaction in computer-mediated communication and face-to-face meetings

This study attempts to present a model of member satisfaction with group decision process. Three variables: ease of use (EOU) of the communication medium; participation; and decision confidence (DC), were explored as determinants of member satisfaction. The study offers an explanation of the effects of these variables on group process satisfaction in two communication media: a nearly synchronous text-based computermediated communication (CMC) and traditional face-to-face communication (FTF). Results indicate that these variables were good predictors of member satisfaction. Regression and correlation analyses help to validate the model for the two communication media using the ordering of the predictor variables and the strength of the relationship. Results confirm that ease of use showed the most contribution to satisfaction and that ease of use is lower in CMC than in FTF. The findings also confirm the effect of communication medium on member satisfaction.     

Visualization in teleimmersive environments

In collaborative virtual reality (VR), the goal is to reproduce a face-to-face meeting in minute detail. Teleimmersion moves beyond this idea, integrating collaborative VR with audio- and video-conferencing that may involve data mining and heavy computation. In teleimmersion, collaborators at remote sites share the details of a virtual world that can autonomously control computation, query databases and gather results. They don't meet in a room to discuss a car engine; they meet in the engine itself. The University of Illinois at Chicago's Electronic Visualization Laboratory (EVL) has hosted several applications that demonstrate rudimentary teleimmersion. All users are members of Cavern (CAVE Research Network) [] $a collection of participating industrial and research institutions equipped with CAVE (Cave Automated Virtual Environment), ImmersaDesk VR systems and high-performance computing resources, including high-speed networks. There are more than 100 CAVE and ImmersaDesk installations worldwide. The pressing challenge now is how to support collaborative work among Cavern users without having them worry about the details of sustaining a collaboration. Another problem is providing both synchronous and asynchronous collaboration. The authors detail how they've built new display devices to serve as more convenient teleimmersion end-points and to support their international networking infrastructure with sufficient bandwidth to support the needs of teleimmersive applications     

Investigating user experience in Second Life for collaborative learning

Two studies on collaborative learning in Second Life are reported. The first is an ecological study of Second Life used in an undergraduate class, by observation, interviews, and limit surveys. Use of Second Life motivated students with good user experience, although they viewed it as a games technology. Second Life was used to prepare virtual meetings and presentations but not for online discussion, with Blackboard and especially Facebook providing collaborative support. In the second experimental study, the effectiveness and user experience with Second Life and Blackboard were compared, including a face-to-face control condition. There were no performance differences overall, although face-to-face was quicker and was preferred by users, followed by Blackboard and Second Life. Blackboard was perceived to be more usable, whereas Second Life provided a better user experience. Worst performance was indicated by dislike of avatar interaction in Second Life, and poor user experience in Blackboard, whereas better performance was associated with engagement with avatars, and better usability in Blackboard. The results of both studies are reviewed using Salmon's model for online learning, suggesting that Second Life helped motivation and socialisation stages, although integration with other technologies is necessary for knowledge construction. Preliminary guidelines are proposed for configuration and management of Second Life in collaborative learning. The affordances for collaboration in virtual worlds are discussed, with reflections on user experience and functional support provided by Second Life, as an exemplar of a virtual world for collaborative learning support.     

An Exploratory Study: Supporting Collaborative Multimedia Systems Design

Multimedia systems design generally requires a collaborative effort from a group of designers with a variety of backgrounds and tasks, such as content experts, instructional designers, media specialists, users, and so forth. However, currently available design tools on the market are mainly designed for a single user. Tools intended to support a collaborative design process should coordinate independent activities of individual designers.This research investigated support for work groups engaged in designing multimedia systems. Specifically, it discussed a new collaborative design environment, called the KMS (Knowledge Management System)-based design environment, in which multimedia designers could share their design knowledge freely. Through two experimental groups, the research investigated impacts of the KMS-based design environment on their collaborative design activities (knowledge creating, knowledge securing, knowledge distributing, and knowledge retrieving activities). The research findings showed that the KMS-based design environment was a promising environment for collaborative multimedia systems design. More specifically, the research findings indicated that the KMS-based design environment supported creating, securing, and retrieving knowledge, but it did not support distributing knowledge. In addition, the research found that the social interactions between group members played important roles in the success of the collaborative multimedia systems design and that the KMS-based design environment did not support the socialization of group members. Furthermore, the research found that the inability of the KMS-based design environment to support the socialization was linked to its low performance level in supporting the knowledge distributing activity. The research explored the desired features of a collaborative support tool for multimedia systems design.     

Experiments with Face-To-Face Collaborative AR Interfaces

We describe a design approach, Tangible Augmented Reality, for developing face-to-face collaborative Augmented Reality (AR) interfaces. Tangible Augmented Reality combines Augmented Reality techniques with Tangible User Interface elements to create interfaces in which users can interact with spatial data as easily as real objects. Tangible AR interfaces remove the separation between the real and virtual worlds, and so enhance natural face-to-face communication. We present several examples of Tangible AR interfaces and results from a user study that compares communication in a collaborative AR interface to more traditional approaches. We find that in a collaborative AR interface people use behaviours that are more similar to unmediated face-to-face collaboration than in a projection screen interface.     

A novel collaborative filtering approach for recommending ranked items

In recent years, Collaborative Filtering (CF) has proven to be one of the most successful techniques used in recommendation systems. Since current CF systems estimate the ratings of not-yet-rated items based on other items’ ratings, these CF systems fail to recommend products when users’ preferences are not expressed in numbers. In many practical situations, however, users’ preferences are represented by ranked lists rather than numbers, such as lists of movies ranked according to users’ preferences. Therefore, this study proposes a novel collaborative filtering methodology for product recommendation when the preference of each user is expressed by multiple ranked lists of items. Accordingly, a four-staged methodology is developed to predict the rankings of not-yet-ranked items for the active user. Finally, a series of experiments is performed, and the results indicate that the proposed methodology produces high-quality recommendations.     

Revisiting the Task-Media Fit Circumflex: A further examination of negotiation tasks

McGrath and Hollingshead's Task-Media Fit Circumflex, which proposed that the negotiations tasks “best fit” highly information-rich channels, is widely accepted for more than two decades. However, this study argues that according to the task nature, different negotiation tasks shall “best fit” different media channels. A 2 × 3 laboratory experiment was conducted to test this argument. Findings suggested that higher process satisfaction, lower cognitive effort and higher negotiation efficiency would be created when text-based systems were used to execute more analyzable tasks. In contrast, less analyzable tasks best fit face-to-face or audio/video-based systems. Managerial implications are discussed accordingly.     

Resource collaboration system based on dynamic user preference and context

In a ubiquitous computing environment, service composition and collaboration among heterogeneous resources are required, thus, an infrastructure that supports these requirements is an essential factor in seamless service delivery. In this environment, users hope to get a variety of customized services by using only an individual mobile device. But the resource of the mobile device has limitations such as tiny display screens, limited input, less powerful processors, and limited storage. Moreover each user situation is different and user preferences are also various. Therefore it is one of new issues to provide a customized service for a user through resource collaboration based on various user preference and situation. To solve this issue, this paper proposes a resource collaboration system which infers customized resources for composing a user required service and collaborative with selected resources. For our collaboration system, this paper proposes the method to infer resources based on the context and user preferences including dynamic change of the preference. This paper also shows a reasonable execution environment for the proposed system through the performance evaluation in server-client and peer-to-peer environments.     

Supporting interactive collaboration on the Web with CORK

The World Wide Web has served as a medium for collaboration since its inception. Web-based collaboration has, however, been dominated by systems supporting asynchronous activities such as sharing documents and participating in discussion forums. Supporting interactive, synchronous collaboration on the Web has proven much more challenging. In this paper we describe three of the challenges encountered in the context of supporting network-based collaboration among middle and high school science students: integrating synchronous and asynchronous modes of interaction, minimizing consumption of bandwidth, and adapting non-collaborative software components for collaborative use. We then present the Content Object Replication Kit, a toolkit for building interactive Java-based collaborative systems for use on the Web.