Meeting browsing

Meeting, to discuss and share information, take decisions and allocate tasks, is a central aspect of human activity. Computer mediated communication offers enhanced possibilities for synchronous collaboration by allowing seamless capture of meetings, thus relieving participants from time-consuming documentation tasks. However, in order for meeting systems to be truly effective, they must allow users to efficiently navigate and retrieve information of interest from recorded meetings. In this article, we review the state of the art in multimedia segmentation, indexing and browsing techniques and show how existing meeting browser systems build on these techniques and integrate various modalities to meet their users’ information needs.     

ARREST: From work practices to redesign for usability

In this paper we discuss the redesign of a support management system deployed in a small and medium sized enterprise (SME) in the UK. The original system was not fulfilling its needs as it had not captured work practices in a way that was recognizable to the users. The advantages of the redesign included: improved usefulness; improved efficiency and productivity; reduced learning time; improved usability; and increased acceptance among users. The system is used to support complex and distributed cooperative activities taking place in an SME. We evaluated the current system and analysed work practices using a user-centred design and evaluation philosophy. In this paper we discuss how user needs are incorporated into the enhanced design of the support management system. The user-centred design techniques used in this research include interviews, questionnaires, observations and user tests. We present comparative evaluation results that show significant improvement in performance of user tasks using the redesigned support management system. The contribution of this paper is the presentation of a case study to show how a user-centred design and evaluation philosophy can lead to better requirements capture resulting in systems that more accurately capture the users’ conceptual models.     

基于信息内容和拓扑关系的社会媒体用户兴趣分类

随着社会的发展,信息已经成为社会发展越来越重要的部分,人类的信息传播活动越来越明显地展示出分众特征,对用户的分类成为人类信息活动的一个重要研究课题.从这一目标出发,分别基于信息内容、拓扑关系和两者综合的方法,按兴趣主题对社会媒体用户进行分类.对于基于信息内容的用户分类,采用LDA主题模型从用户所发布的内容中提取其主题分布,基于这一分布,采用支持向量机、决策树、贝叶斯等多种模型按兴趣主题对用户进行分类.对于基于拓扑关系的分类,依据相同兴趣主题的用户倾向于拥有共同的粉丝这一发现,构建分类模型来按兴趣主题对用户进行分类.然后提出综合信息内容和拓扑关系的分类方法来对用户进行分类.最后基于大规模Twitter数据的实验发现,采用综合方法对用户进行的兴趣分类性能明显高于采用单一信息内容或粉丝拓扑方法的性能.     

Efficient fuzzy full-text type-ahead search

Traditional information systems return answers after a user submits a complete query. Users often feel “left in the dark” when they have limited knowledge about the underlying data and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step toward solving this problem. In this paper, we study a new information-access paradigm, called “type-ahead search” in which the system searches the underlying data “on the fly” as the user types in query keywords. It extends autocomplete interfaces by allowing keywords to appear at different places in the underlying data. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms for both single-keyword queries and multi-keyword queries, using previously computed and cached results in order to achieve a high interactive speed. We develop novel techniques to support fuzzy search by allowing mismatches between query keywords and answers. We have deployed several real prototypes using these techniques. One of them has been deployed to support type-ahead search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.     

Involving users in expert system development

Abstract: The ultimate criterion of success for interactive expert systems is that they will be used, and used to effect, by individuals other than the system developers. A key ingredient of success in most systems is involving users in the specification and development of systems as they are being built. However, until recently, system designers have paid little attention to ascertaining user needs and to developing systems with corresponding functionality and appropriate interfaces to match those requirements. Although the situation is beginning to change, many developers do not know how to go about involving users, or else tackle the problem in an inadequate way. This paper discusses the need for user involvement and considers why many developers are still not involving users in an optimal way. It looks at the different ways in which users can be involved in the development process and describes how to select appropriate techniques and methods for studying users. Finally, it discusses some of the problems inherent in involving users in expert system development, and recommends an approach which incorporates both ethnographic analysis and formal user testing.     

Evaluating the value of collaboration systems in collocated teams: A longitudinal analysis

Distributed work structure has increased the importance of group work. Consequently, the usage of collaboration systems that support group work has increased exponentially. However, limited research exists on why collocated group members use collaborative system and how their perceptions about the system change from project initiation to project completion. The study proposes resource management support, coordination support, and evaluation support as key aspects based on which collocated users form beliefs about collaboration system value. Based on technology acceptance model and expectancy disconfirmation theory, we propose a model that examines the evolution of user beliefs from pre to post-adoption stage. Further, we argue that users may engage in different information processing strategies as part of the belief change process. The research model was examined based on longitudinal data collected from student groups working on a software application development project over a three month period. The results show that users deemed all features to be valuable at the pre-adoption stage. However, at the post-adoption stage evaluation support was the only factor that had a direct influence on collaboration system usefulness. While the impact of other support factors was mediated through the confirmation process. Further, users engaged in high information processing in revising their beliefs about collaboration system features, when they experienced a high level of dissonance. On the contrary, a dual information processing approach was visible in the context of support features that required adjustment or update to beliefs from pre to post-adoption stage. The study provides three core areas around which designers can structure collaboration system features to make it more useful. It also provides theoretical insights into the process through which user cognitions about collaboration system value change over time.     

A new approach for combining content-based and collaborative filters

With the development of e-commerce and the proliferation of easily accessible information, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendations, including content-based and collaborative techniques. Content-based filtering selects information based on semantic content, whereas collaborative filtering combines the opinions of other users to make a prediction for a target user. In this paper, we describe a new filtering approach that combines the content-based filter and collaborative filter to capitalize on their respective strengths, and thereby achieves a good performance. We present a series of recommendations on the selection of the appropriate factors and also look into different techniques for calculating user-user similarities based on the integrated information extracted from user profiles and user ratings. Finally, we experimentally evaluate our approach and compare it with classic filters, the result of which demonstrate the effectiveness of our approach.     

Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and web browsing

Serendipity is the making of fortunate discoveries by accident, and is one of the cornerstones of scientific progress. In today's world of digital data and media, there is now a vast quantity of material that we could potentially encounter, and so there is an increased opportunity of being able to discover interesting things. However, the availability of material does not imply that we will be able to actually find it; the sheer quantity of data mitigates against us being able to discover the interesting nuggets.This paper explores approaches we have taken to support users in their search for interesting and relevant information. The primary concept is the principle that it is more useful to augment user skills in information foraging than it is to try and replace them. We have taken a variety of artificial intelligence, statistical, and visualisation techniques, and combined them with careful design approaches to provide supportive systems that monitor user actions, garner additional information from their surrounding environment and use this enhanced understanding to offer supplemental information that aids the user in their interaction with the system.We present two different systems that have been designed and developed according to these principles. The first system is a data mining system that allows interactive exploration of the data, allowing the user to pose different questions and understand information at different levels of detail. The second supports information foraging of a different sort, aiming to augment users browsing habits in order to help them surf the internet more effectively. Both use ambient intelligence techniques to provide a richer context for the interaction and to help guide it in more effective ways: both have the user as the focal point of the interaction, in control of an iterative exploratory process, working in indirect collaboration with the artificial intelligence components.Each of these systems contains some important concepts of their own: the data mining system has a symbolic genetic algorithm which can be tuned in novel ways to aid knowledge discovery, and which reports results in a user-comprehensible format. The visualisation system supports high-dimensional data, dynamically organised in a three-dimensional space and grouped by similarity. The notions of similarity are further discussed in the internet browsing system, in which an approach to measuring similarity between web pages and a user's interests is presented. We present details of both systems and evaluate their effectiveness.     

Design and evaluation of a gamified system for ERP training

Enterprise Resource Planning Systems (ERP) facilitate the flow of information within a company by storing data in common databases. These systems offer a holistic view of the organization because they reduce information redundancy, offer information in real time, help with process standardization, and improve information flow and communication among employees. Nevertheless, the benefits attributed to an ERP implementation can be lost without an effective user training. Previous studies have observed that common training mechanisms don't provide meaningful learning to users, and that user satisfaction rates range from neutral to low. Therefore, in this paper we study the effects that gamification has on ERP training concerning user learning and user satisfaction. Gamification is the use of game elements and game design techniques in non-gaming environments. Several applications prove that gamified systems increase user engagement and performance. Our hypotheses were that a gamified system for ERP training improve user learning and user satisfaction levels during the training period. To test our hypotheses, we designed and evaluated a gamified system. The results showed that users trained using a gamified system performed better than those trained using a conventional, non-gamified, training mechanism.     

A privacy-preserving recommendation method with clustering and locality-sensitive hashing

Nowadays, there is a significant increase in information, resulting in information overload. Recommendation systems have been widely adopted, and they can help users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. To solve problems of data sparseness, enormous high-dimensional data, the cold start problem and privacy protection in an intelligent recommender system, this study proposes a privacy-preserving collaborative filtering recommendation method with clustering and locality-sensitive hashing. First, we cluster users according to their characteristic information to obtain sub-rating matrices. We use the latent factor model to predict and fill in the missing ratings in those matrices. Second, we combine the sub-rating matrices into a complete rating matrix, subsequently, we obtained the neighbors of the target user by analyzing the similarity of the users. We use a locality-sensitive hashing algorithm to reduce the dimensionality of the user rating data and build an index that could quickly obtain the neighbors of the target user. Finally, we predict the target user's ratings and provide recommendations to the target user. Through experiments, our study shows that our method can deal with the problems of data sparseness and cold start problems well and the accuracy of the intelligent recommendation system has been improved. In addition, we use hash techniques to search for the neighbors, which effectively protects the privacy of the user.     

User viewpoint modelling: understanding and representing user viewpoints during requirements definition

There has been increasing awareness of the impact of the early stages of systems development on the quality of information systems. A critical early activity is requirements definition, when the requirements for an information system are determined. Traditional requirements capture techniques do not support the collaborative nature of requirements definition or the emergent nature of requirements themselves. This paper focuses on viewpoint development as a means of resolving some of the difficulties of requirements definition. It proposes a user viewpoint model for capturing and representing the viewpoints of users during requirements acquisition. The model can facilitate communication and interaction between analysts and users and help build a shared understanding of requirements. It can be used to structure the requirements acquisition process. The model provides for evaluation of requirements acquisition techniques to guide the selection of appropriate techniques for developing user viewpoint models. The paper reports a multiple-case study of requirements definition efforts that examined user viewpoint development in practice and used the cases to validate empirically the concepts of the user viewpoint model. The implications of the case study findings for requirements definition practice are discussed, and some areas for future research are identified.     

Using shared representations to improve coordination and intent inference

In groupware, users must communicate about their intentions and aintain common knowledge via communication channels that are explicitly designed into the system. Depending upon the task, generic communication tools like chat or a shared whiteboard may not be sufficient to support effective coordination. We have previously reported on a methodology that helps the designer develop task specific communication tools, called coordinating representations, for groupware systems. Coordinating representations lend structure and persistence to coordinating information. We have shown that coordinating representations are readily adopted by a user population, reduce coordination errors, and improve performance in a domain task. As we show in this article, coordinating representations present a unique opportunity to acquire user information in collaborative, user-adapted systems. Because coordinating representations support the exchange of coordinating information, they offer a window onto task and coordination-specific knowledge that is shared by users. Because they add structure to communication, the information that passes through them can be easily exploited by adaptive technology. This approach provides a simple technique for acquiring user knowledge in collaborative, user-adapted systems. We document our application of this approach to an existing groupware system. Several empirical results are provided. First, we show how information that is made available by a coordinating representation can be used to infer user intentions. We also show how this information can be used to mine free text chat for intent information, and show that this information further enhances intent inference. Empirical data shows that an automatic plan generation component, which is driven by information from a coordinating representation, reduces coordination errors and cognitive effort for its users. Finally, our methodology is summarized, and we present a framework for comparing our approach to other strategies for user knowledge acquisition in adaptive systems.     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

Modeling the user state for context-aware spoken interaction in ambient assisted living

Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfill these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user’s state. The user’s state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user’s needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.     

Adaptive hypertext navigation based on user goals and context

Hypertext systems allow flexible access to topics of information, but this flexibility has disadvantages. Users often become lost or overwhelmed by choices. An adaptive hypertext system can overcome these disadvantages by recommending information to users based on their specific information needs and preferences. Simple associative matrices provide an effective way of capturing these user preferences. Because the matrices are easily updated, they support the kind of dynamic learning required in an adaptive system.HYPERFLEX, a prototype of an adaptive hypertext system that learns, is described. Informal studies with HYPERFLEX clarify the circumstances under which adaptive systems are likely to be useful, and suggest that HYPERFLEX can reduce time spent searching for information by up to 40%. Moreover, these benefits can be obtained with relatively little effort on the part of hypertext authors or users.The simple models underlying HYPERFLEX's performance may offer a general and useful alternative to more sophisticated modelling techniques. Conditions under which these models, and similar adaptation techniques, might be most useful are discussed.     

Navigation techniques for small-screen devices: An evaluation on maps and web pages

Several techniques have been proposed to support user navigation of large information spaces (e.g., maps or web pages) on small-screen devices such as PDAs and Smartphones. In this paper, we present the results of an evaluation that compared three of these techniques to determine how they might affect performance and satisfaction of users. Two of the techniques are quite common on mobile devices: the first one (DoubleScrollbar) is the standard combination of two scrollbars for separate horizontal and vertical scrolling with zoom buttons to change the scale of the information space, the second one (Grab&Drag) enables users to navigate the information space by directly dragging its currently displayed portion, while zooming is handled through a slider control. The last technique (Zoom-Enhanced Navigator or ZEN) is an extension and adaptation to mobile screens of Overview&Detail approaches, which are based on displaying an overview of the information space together with a detail view of a portion of that space. In these approaches, navigation is usually supported by (i) highlighting in the overview which portion of space is displayed in the detail view, and (ii) allowing users to move the highlight within the overview area to change the corresponding portion of space in the detail area. Our experimental evaluation concerned tasks involving maps as well as web page navigation. The paper analyzes in detail the obtained results in terms of task completion times, number and duration of user interface actions, accuracy of the gained spatial knowledge, and subjective preferences.     

Knowledge-based sense disambiguation (almost) for all structures

Structural disambiguation is acknowledged as a very real and frequent problem for many semantic-aware applications. In this paper, we propose a unified answer to sense disambiguation on a large variety of structures both at data and metadata level such as relational schemas, XML data and schemas, taxonomies, and ontologies. Our knowledge-based approach achieves a general applicability by converting the input structures into a common format and by allowing users to tailor the extraction of the context to the specific application needs and structure characteristics. Flexibility is ensured by supporting the combination of different disambiguation methods together with different information extracted from different sources of knowledge. Further, we support both assisted and completely automatic semantic annotation tasks, while several novel feedback techniques allow us to improve the initial disambiguation results without necessarily requiring user intervention. An extensive evaluation of the obtained results shows the good effectiveness of the proposed solutions on a large variety of structure-based information and disambiguation requirements.     

User evaluation of information systems: by system typology

The evaluation of information systems is often conducted using performance measures developed by the system designers. Still, other categories of measures exist, including those that match the system to the desires of the user, the climate of the organization, and the decision quality. This study looks at the importance to the user of different evaluation categories for three system types. Findings indicate that users value decision quality characteristics for systems intended to support the decision process, but otherwise lean toward using performance measures. These results argue in favour of user evaluation for certain system types     

Planning and controlling the performance analysis of a large teleprocessing,production computer system

The performance analysis of a large computer production system at IBM is described. It employs a System/370 Model 155 in the Information Management System (IMS) program product which is used to support manufacturing, finance, and document control, and employs multiple terminals. A major objective of the performance analysis is to provide a data center function with the information to predict resource (the System/370 Model 155 CPU and channels) utilization and terminal response time characteristics for various teleprocessing rates. The Program Evaluation and Review Technique (PERT) was used by the programmers, systems users, and data center to study the performance analysis plan at preselected points in time and review the progress of the plan in a practical, reality-oriented manner. The performance analysis plan provided management and technical people from various functions with a definitive statement which was used to periodically evaluate project progress in meeting the original technical and scheduled objectives. It served as a valuable communication base for all involved. The PERT checkpoint reviews insured that we were using the programmers and user data and information correctly, determine if everyone was still in agreement, and reaffirm that the planned activities were understood and supported. These PERT review meetings served as a basis for updating, deleting, and adding hardware, software, and applications information when required. The PERT review meetings were documented and provided an audit trail of progress. The performance analysis itself will be described, which is structured to be carried out in stages—information requirements, information collection, manual performance analysis, and finally the full performance analysis using a computer model. This step-by-step approach ensured that the analysis could be continued, changed, or stopped at different checkpoints in the overall project cycle.     

Extracting relevance and affect information from physiological text annotation

We present physiological text annotation, which refers to the practice of associating physiological responses to text content in order to infer characteristics of the user information needs and affective responses. Text annotation is a laborious task, and implicit feedback has been studied as a way to collect annotations without requiring any explicit action from the user. Previous work has explored behavioral signals, such as clicks or dwell time to automatically infer annotations, and physiological signals have mostly been explored for image or video content. We report on two experiments in which physiological text annotation is studied first to (1) indicate perceived relevance and then to (2) indicate affective responses of the users. The first experiment tackles the user’s perception of relevance of an information item, which is fundamental towards revealing the user’s information needs. The second experiment is then aimed at revealing the user’s affective responses towards a -relevant- text document. Results show that physiological user signals are associated with relevance and affect. In particular, electrodermal activity was found to be different when users read relevant content than when they read irrelevant content and was found to be lower when reading texts with negative emotional content than when reading texts with neutral content. Together, the experiments show that physiological text annotation can provide valuable implicit inputs for personalized systems. We discuss how our findings help design personalized systems that can annotate digital content using human physiology without the need for any explicit user interaction.