Personalization and Context Management

Supporting the individual user in his working, learning, or information access is one of the main goals of user modeling. Personal or group user models make it possible to represent and use information about preferences, knowledge, abilities, emotional states, and many other characteristics of a user to adapt the user experience and support. Nowadays, the disappearing computer enables the user to access her information from a variety of personal and public displays and devices. To support a new generation of contextualized and personalized information and services, this paper addresses the problem of context management. Context management is a new approach to the design of context-aware systems in ubiquitous computing that combines personalization and contextualization. The presented framework for context management integrates user modeling and context modeling, which can benefit from each other and give rise to more valid models for personalized and contextualized information delivery. The paper will introduce a base framework and tools for designing context-aware applications and decompose the underlying framework into its foundational components. As two illustrative application cases, the paper discusses implementations of an intelligent advertisement board and an audio-augmented museum environment.     

Contextualized indicators for online failure diagnosis in cellular networks

This paper presents a novel approach for self-healing in cellular networks based on the application of mobile terminals context information: time, service, activity, identity and, especially, location. Context information is therefore used to support root cause analysis, providing improved network fault diagnosis compared to classical non-context-aware approaches. The integration of context information is implemented by means of the newly defined contextualized indicators. These are used in order to integrate user equipment context information in pre-existent failure management schemes. The presented techniques are especially suitable for indoor small cell scenarios, whose particular conditions of dynamic user distribution, overlapping coverage, dynamic radio and service provisioning environment, etc., make previous diagnosis schemes especially unreliable. The algorithms and methodology for the proposed context-aware system are defined and its performance is assessed by means of an LTE system-level simulator.     

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.     

Agent-based architecture for context-aware and personalized event recommendation

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.     

一种上下文移动用户偏好自适应学习方法

针对移动网络对个性化移动网络服务系统的性能提出了更高的要求,但现有研究难以自适应地修改上下文移动用户偏好以为移动用户提供实时、准确的个性化移动网络服务的问题,提出了一种上下文移动用户偏好自适应学习方法,在保证精确度的基础上缩短了学习的响应时间.首先,通过分析移动用户行为日志来判断移动用户行为是否受上下文影响,并在此基础上判断移动用户行为是否发生变化.然后,根据判断结果对上下文移动用户偏好进行修正.在对发生变化的上下文移动用户偏好进行学习时,将上下文引入到最小二乘支持向量机中,进一步提出了基于上下文最小二乘支持向量机(C-LSSVM)的上下文移动用户偏好学习方法.最后,实验结果表明,当综合考虑精确度和响应时间两方面因素时,所提出的方法优于其他学习方法,并且可应用于个性化移动网络服务系统中.     

面向移动应用的探索式个性化服务推荐方法

个性化推荐系统是应用系统中广泛应用的技术之一,用户兴趣偏好模型的建立与更新是个性化推荐系统的关键环节,针对移动设备位置随时变化的特点,以移动端的应用系统为研究对象,提出了一种随用户位置变化而动态更新的用户兴趣偏好模型,并对实现过程中的几个关键问题,包括用户兴趣偏好模型表示方法、用户兴趣关键字提取、用户兴趣偏好模型的建立与更新算法进行了详细描述,最后利用用户兴趣偏好模型根据协同过滤算法进行个性化推荐,并根据用户对推荐结果的评价进一步修正用户兴趣偏好模型.用户兴趣偏好模型采用基于兴趣关键字的向量空间模型表示,用户兴趣关键字由根据TF-IDF算法获得的用户隐式兴趣和用户参与的显式兴趣相结合获得,用户位置信息变化时,系统获取当前位置附近的服务,对已存在于用户兴趣关键树中的服务权值进行增强,而对不存在其中的进行遗忘以调整用户兴趣树从而更新用户兴趣偏好模型.验证表明,该方法推荐的服务更符合用户所处的位置上下文环境,并且具有高度的可达性.     

基于短期记忆与遗忘系数的用户个性化建模方法研究

搜索引擎的一个标准是不同的用户用相同的查询条件检索时,返回的结果相同。为解决准确性问题,个性化搜索引擎被提出,它可以根据用户的不同个性化特征提供不同的搜索结果。然而,现有的方法更注重用户的长时记忆和独立的用户日志文件,从而降低了个性化搜索的有效性。获取用户短时记忆模型来提供准确有效的用户偏好的个性化搜索方法被广泛采用。首先,根据基于查询关键词的相关概念生成短期记忆模型;接着,基于用户的时序有效点击数据生成用户个性化模型;最后,在用户会话中引入了遗忘因子来优化用户个性化模型。实验结果表明,所提出的方法可以较好地表达用户信息需求,较为准确地构建用户的个性化模型。     

Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective

Mobile banking (m-banking) is an expanding application of mobile commerce that has claimed the attention and interest of e-commerce researchers. One of the most welcome recent developments in m-banking has been the growing interest in end-user use, user satisfaction, and individual performance. We propose a model combining the DeLone & McLean IS success model and the Task Technology Fit (TTF) model to evaluate the impact of m-banking on individual performance. The empirical approach is based on an online survey questionnaire of 233 individuals. The results reveal that use and user satisfaction are important precedents of individual performance, and the importance of the moderating effects of TTF over usage to individual performance. The system quality, information quality, and service quality positively affect user satisfaction. Understanding the significance of m-banking context on individual performance is useful to provide new insight to m-banking managers to apply strategies to retain users or even attract potential adopters. We provide the theoretical and practical implications of our findings.     

Context-aware control of user-centric virtual networks: Centralized vs distributed approaches

Recently, the Internet has been overrun by a diversity of devices, applications, technologies, and Internet users with increasingly demanding personal preferences. Due to the explosive growth of Internet usage, the quantity of context information available in networking environments is also growing rapidly, triggering research for novel architectures and protocols that are enriched or personalized based on the particular features and dynamics of such information.This article describes a virtualized architecture that splits a physical network infrastructure into a set of logical networks (or Virtual Networks – VNs) configured to meet the particular context needs of their attached users (in terms of, e.g., price, security or services’ quality). Since this architecture can be driven by volatile context needs of highly mobile users, we also present the signaling mechanisms to create, extend and remove VNs in response to user context dynamics and mobility, which can be performed in a centralized or distributed way. Further, we define and evaluate context-aware metrics to configure a VN, and discover and select VNs to assign to users or network paths.The evaluation of the proposed approach shows that distributed approaches allow the fast discovery and adaptation of VNs, at the cost of a slightly larger overhead than centralized approaches when the context dynamics are too high. We also assess the impact of considering distinct levels of knowledge distribution and user context dynamics on the design and behavior of several processes for user association and VN control. Finally, we observe that context-driven VN discovery and resource-aware path selection schemes outperform the ones that, respectively, flood the network with VN discovery requests or use shortest path-based strategies to adapt VNs.     

Providing user context for mobile and social networking applications

The processing capabilities of mobile devices coupled with portable and wearable sensors provide the basis for new context-aware services and applications tailored to the user environment and daily activities. In this article, we describe the approach developed within the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth to provide user contexts. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information in web-centered servers that support well-known social networking services. In the current prototype, context inference is based on decision trees to learn and to identify contexts dynamically at run-time, but the middleware allows the integration of different inference engines if necessary. Experimental results in a real-world setting suggest that the proposed solution is a promising approach to provide user context to local mobile applications as well as to network-level applications such as social networking services.     

Personalized blog content recommender system for mobile phone users

Compared to newspaper columnists and broadcast media commentators, bloggers do not have organizations actively promoting their content to users; instead, they rely on word-of-mouth or casual visits by web surfers. We believe the WAP Push service feature of mobile phones can help bridge the gap between internet and mobile services, and expand the number of potential blog readers. Since mobile phone screen size is very limited, content providers must be familiar with individual user preferences in order to recommend content that matches narrowly defined personal interests. To help identify popular blog topics, we have created (a) an information retrieval process that clusters blogs into groups based on keyword analyses, and (b) a mobile content recommender system (M-CRS) for calculating user preferences for new blog documents. Here we describe results from a case study involving 20,000 mobile phone users in which we examined the effects of personalized content recommendations. Browsing habits and user histories were recorded and analyzed to determine individual preferences for making content recommendations via the WAP Push feature. The evaluation results of our recommender system indicate significant increases in both blog-related push service click rates and user time spent reading personalized web pages. The process used in this study supports accurate recommendations of personalized mobile content according to user interests. This approach can be applied to other embedded systems with device limitations, since document subject lines are elaborated and more attractive to intended users.     

Modeling user behavior data in systems of engagement

The proliferation of mobile devices has changed the way digital information is consumed and its efficacy measured. These personal devices know a lot about user behavior from embedded sensors along with monitoring the daily activities users perform through various applications on these devices. This data can be used to get a deep understanding of the context of the users and provide personalized services to them. However, there are a lot of challenges in capturing, modeling, storing, and processing such data from these systems of engagement, both in terms of achieving the right balance of redundancy in the captured and stored data, along with ensuring the usefulness of the data for analysis. There are additional challenges in balancing how much of the captured data should be processed through client or server applications. In this article, we present the modeling of user behavior in the context of personalized education which has generated a lot of recent interest. More specifically, we present an architecture and the issues of modeling student behavior data, captured from different activities the student performs during the process of learning. The user behavior data is modeled and sent to the cloud-enabled backend where detailed analytics are performed to understand different aspects of a student, such as engagement, difficulties, and preferences and to also analyze the quality of the data.     

A knowledge infrastructure for intelligent query answering in location-based services

Intelligent query answering in Location-based Services refers to their capability to provide mobile users with personalized and contextualized answers. Personalization is expected to lead to answers that better match user’s interests, as inferable from the user’s profile. Contextualization aims at not selecting answers that for some reason would not be appropriate at the time and place of the user query. These goals are beyond the current state of art in LBS, or are provided based on ad hoc solutions specific to the application at hand. This paper reports on the results of an investigation aiming at defining the knowledge infrastructure that should be developed within the LBS to make it capable of returning intelligent answers. We first discuss the data management features that make LBS different from other query answering systems. Next we propose a data infrastructure that builds on the idea of modular ontologies. We explain how the relevant knowledge may be incrementally set up and dynamically maintained based on an application-independent approach. Last we show how this knowledge is used to reformulate user’s queries via personalized and contextualized rewriting.     

基于用户操作行为的兴趣度的分析与计算

在互联网的背景下,用户检索行为所体现的兴趣是零散的、分布的.利用一个群集模型来综合这些分布的信息,对个性化服务也会提供帮助.通过对单个用户行为的分析,提出了一种基于操作行为的兴趣度的计算方法,可以有效地计算出该用户对当前内容的兴趣度的基值,并最终为用户兴趣群集模型中各个结点的兴趣度的值的计算提供重要依据.     

基于情境相似度和二次聚类的协同过滤推荐算法

随着移动互联网规模的不断扩大,传统推荐系统因较少考虑多种情境因素和用户置信度对用户偏好预测的综合影响,造成了推荐算法预测结果的偏差。针对此问题,将情境信息引入个性化推荐的过程中,提出一种基于情境相似度和二次聚类的协同过滤算法。该算法首先根据用户情境的相似度对用户进行初始聚类,再基于评分矩阵计算用户评分置信度,将用户分为核心用户和非核心用户;然后根据核心用户评分对初始聚类的簇心进行调整,并对簇中非核心用户进行重聚类,形成新的聚簇;最终根据情境相似度对用户偏好进行预测。该算法可以在一定程度上降低评分矩阵中的噪点对聚类结果的影响,提高了推荐结果的准确性。基于实际数据集的仿真实验表明,该算法与传统协同过滤算法相比能够有效提高用户偏好预测的准确性,增加协同过滤推荐算法的精确度。     

Exploring the benefits of the combination of a software architecture analysis and a usability evaluation of a mobile application

Designing easy to use mobile applications is a difficult task. In order to optimize the development of a usable mobile application, it is necessary to consider the mobile usage context for the design and the evaluation of the user-system interaction of a mobile application. In our research we designed a method that aligns the inspection method “Software ArchitecTure analysis of Usability Requirements realizatioN” SATURN and a mobile usability evaluation in the form of a user test. We propose to use mobile context factors and thus requirements as a common basis for both inspection and user test. After conducting both analysis and user test, the results described as usability problems are mapped and discussed. The mobile context factors identified define and describe the usage context of a mobile application. We exemplify and apply our approach in a case study. This allows us to show how our method can be used to identify more usability problems than with each method separately. Additionally, we could confirm the validity and identified the severity of usability problems found by both methods. Our work presents how a combination of both methods allows to address usability issues in a more holistic way. We argue that the increased quantity and quality of results can lead to a reduction of the number of iterations required in early stages of an iterative software development process.     

A prediction and auto-execution system of smartphone application services based on user context-awareness

Smartphones have emerged as suitable environments for user context-awareness and intelligent service provision due to the high penetration rate, the high usability, various embedded sensors, and so on. In particular, its most unique characteristic is the usage of various applications. However, the most of existing studies through the three steps process (log collection, context inference, and service provision) did not consider smartphone applications (Apps) as the target service. Smartphone users still have to use Apps with manual controls by own decision. Therefore, in this paper, we propose a system to predict smartphone applications based on inferring user context. We define a mobile context model with a new level of context (Situation) and its inference method to perceive a user’s intention or purpose related to the App usage. Based on the Situation context, the system predicts Apps which can be useful and helpful for a user and automatically executes it on his/her smartphone. With the proposed system, it will be possible to autonomously provide and manage smartphone application services without users’ perception or intervention.     

Mobile guides: Taxonomy of architectures,context awareness,technologies and applications

Portable devices are increasingly employed in a wide range of mobile guidance applications. Typical examples are guides in urban areas, museum guides, and exhibition space aids. The demand is for the delivery of context-specific services, wherein the context is typically identified by a combination of data related to location, time, user profile, device profile, network conditions and usage scenario. A context-aware mobile guide is intended to provide guidance services adjusted to the context of the received request. The adjustment may refer to tailoring the user interface to the perceived context, as well as delivering the right type of information to the right person at the right time and the right location. It may also refer to intermediary adaptation, as in the case of mobile multimedia transmission. This paper offers a taxonomy of mobile guides considering multiple criteria. The taxonomy considers several aspects of the mobile applications space, including context awareness, client architectures, mobile user interfaces, as well as offered functionalities, highlighting functional, architectural, technological, and implementation issues. Existing implementations are classified accordingly and a discussion of research issues and emerging trends is offered.     

Contextual influences on user satisfaction with mobile computing: findings from two healthcare organizations

Mobile information technologies (IT) are transforming individual work practices and organizations. These devices are extending not only the boundaries of the ‘office’ in space and time, but also the social context within which use occurs. In this paper, we investigate how extra-organizational influences can impact user satisfaction with mobile systems. The findings from our longitudinal study highlight the interrelatedness of different use contexts and their importance in perceptions of user satisfaction. The data indicate that varying social contexts of individual use (individual as employee, as professional, as private user, and as member of society) result in different social influences that affect the individual's perceptions of user satisfaction with the mobile technology. While existing theories explain user satisfaction with IT within the organizational context, our findings suggest that future studies of mobile IT in organizations should accommodate such extra-organizational contextual influences.     

Designing personal attentive user interfaces in the mobile public safety domain

In the mobile computing environment, there is a need to adapt the information and service provision to the momentary attentive state of the user, operational requirements and usage context. This paper proposes to design personal attentive user interfaces (PAUI) for which the content and style of information presentation is based on models of relevant cognitive, task, context and user aspects. Using the police work environment as the application domain, relevant attributes of these aspects are identified based on literature and domain analyses. We present a user-centered design (UCD) method for the iterative development and validation of the proposed PAUI. Application of this approach provided requirements for (1) adaptation to users’ attentive state, (2) notification, (3) information processing and task switching support and (4) user modeling. We aim at refining and validating the models and requirements through continuing empirical evaluation.