基于上下文感知的普适服务框架

为了更好地适应用户的个性化需求和普适计算环境的特征,提出一种基于上下文感知的普适服务框架。该框架包括服务呈现层、服务管理层、服务提供层和上下文感知层。通过基于社区的服务管理方式来屏蔽普适计算环境中服务的异构性、分布性,动态地感知与当前计算环境和用户活动相关的上下文信息,采用基于上下文的服务推荐机制为用户提供个性化的服务,从而更好地适应人的意图和环境 因素。     

A content search system considering the activity and context of a mobile user

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user’s activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user’s degree of concentration on his or her mobile device’s display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user’s activity and context, with the goal of reducing the user’s operation load for content search. The proposed system switches between two kinds of content search systems according to the user’s activity: the location-based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu-based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system.     

Inferring contextual preferences using deep encoder-decoder learners

ABSTRACT

Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.     

EARS: Emotion-aware recommender system based on hybrid information fusion

Recommender systems suggest items that users might like according to their explicit and implicit feedback information, such as ratings, reviews, and clicks. However, most recommender systems focus mainly on the relationships between items and the user’s final purchasing behavior while ignoring the user’s emotional changes, which play an essential role in consumption activity. To address the challenge of improving the quality of recommender services, this paper proposes an emotion-aware recommender system based on hybrid information fusion in which three representative types of information are fused to comprehensively analyze the user’s features: user rating data as explicit information, user social network data as implicit information and sentiment from user reviews as emotional information. The experimental results verify that the proposed approach provides a higher prediction rating and significantly increases the recommendation accuracy.     

A novel mobile recommender system for indoor shopping

With the widespread usage of mobile terminals, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which continues to be the main shopping mode. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users’ preferences by analyzing users’ positions, without requiring users’ explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows the new proposed mobile recommender system achieves much better user satisfaction than the benchmark method, without losing obvious recommendation performances.     

Customising WAP-based information services on mobile networks

In addition to voice transmission over mobile networks, the demand of data communication has been increasing. To deploy data-oriented applications for mobile terminals, the wireless application protocol (WAP) has provided a promising solution. However, as in the World Wide Web (WWW), the increasing information leads to the problem of information overload. One way to overcome such a problem is to build intelligent recommender systems to provide customised information services. By analyzing the information collected from the user, a customised recommender system is able to reason his personal preferences and to build a model of predictions. In this way, only the information predicted as user-interested can reach the end user. This paper presents a multi-agent framework in which a decision tree-based approach is employed to learn a users preferences. To assess the proposed framework, a mobile phone simulator is used to represent a mobile environment and a series of experiments are conducted. The experimental studies have concentrated on how to recommend appropriate information to the individual user, and on how the system can adapt to a users most recent preferences. The results and analysis show that based on our framework the WAP-based customised information services can be successfully performed.     

Context relevance assessment and exploitation in mobile recommender systems

In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.     

Hybreed: A software framework for developing context-aware hybrid recommender systems

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.     

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.     

Contextual music information retrieval and recommendation: State of the art and challenges

Increasing amount of online music content has opened new opportunities for implementing new effective information access services–commonly known as music recommender systems–that support music navigation, discovery, sharing, and formation of user communities. In the recent years a new research area of contextual (or situational) music recommendation and retrieval has emerged. The basic idea is to retrieve and suggest music depending on the user’s actual situation, for instance emotional state, or any other contextual conditions that might influence the user’s perception of music. Despite the high potential of such idea, the development of real-world applications that retrieve or recommend music depending on the user’s context is still in its early stages. This survey illustrates various tools and techniques that can be used for addressing the research challenges posed by context-aware music retrieval and recommendation. This survey covers a broad range of topics, starting from classical music information retrieval (MIR) and recommender system (RS) techniques, and then focusing on context-aware music applications as well as the newer trends of affective and social computing applied to the music domain.     

移动推荐系统及其应用

近年来,移动推荐系统已成为推荐系统研究领域最为活跃的课题之一.如何利用移动上下文、移动社会化网络等信息进一步提高移动推荐系统的推荐精确度和用户满意度,成为移动推荐系统的主要任务.对最近几年移动推荐系统研究进展进行综述,对其关键技术、效用评价以及应用实践等进行前沿概括、比较和分析.最后,对移动推荐系统有待深入的研究难点和发展趋势进行分析和展望.     

A Graphical Model for Context-Aware Visual Content Recommendation

Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the user's contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the user's need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval.     

Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

基于用户轨迹数据的移动推荐系统研究

近年来,随着移动智能设备的普及,移动社交网络方兴未艾,用户习惯和朋友分享自己的精彩经历,因此产生了大规模具有时空属性的用户轨迹数据.从狭义的角度来看,轨迹数据是指连续采样的GPS数据.从广义的角度来看,在时空域存在连续性的序列,都可以称作轨迹.例如：在社交网络上的用户签到序列就可以认为是粗粒度的轨迹数据.广义轨迹数据具有时空异构性、连续与离散并存、时空项目的层次性不明显和分类不明确等特点,但是相比于GPS轨迹数据,广义轨迹数据来源广泛,蕴含丰富的信息,这给传统的移动推荐系统带来了巨大的机遇.与此同时,广义轨迹数据规模大、结构丰富,这也给传统的移动推荐系统带来了巨大的挑战.如何利用广义用户轨迹数据来提升移动推荐系统的性能,已成为学术界和产业界共同关注的重要课题.以轨迹数据特征作为切入点,对近年来基于广义用户轨迹数据的移动推荐系统的主要模型方法和推荐评价指标进行了系统综述,阐述了与传统移动推荐系统的联系和区别.最后,对基于广义用户轨迹数据的移动推荐系统有待深入研究的难点和发展趋势进行了分析和展望.     

Design and validation of a light inference system to support embedded context reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.     

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.     

Examining Factors Influencing the Disruptiveness of Notifications in a Mobile Museum Context

Ubiquitous computing environments continuously infer our context and proactively offer us context aware services and information, suggested by notifications on our mobile devices. However, notifications come with a cost. They may interrupt the user in the current task and be annoying in the wrong context. The challenge becomes how to notify the user about the availability of relevant services while minimizing the level of disruptiveness. Thus, an understanding of what affects the subjective perception of the disruptiveness of the notification is needed. As yet, most of the research on disruptiveness of notifications focused on stationary, task-oriented environments. In this study, we examine the effect of notifications in a special leisure scenario—a museum visit. In two user studies conducted in a museum setting, participants used a context-aware mobile museum guide to receive information on various museum exhibits while periodically receiving notifications. We examined how the user’s activity, the modality of the notification, and the message content affected the perceived level of disruption that the notifications created. We discuss our results in light of existing work in the desktop and mobile domains and provide a framework and recommendations for designing notifications for a mobile museum guide system.     

基于移动网络的混合型信息推送系统的研究

为了解决在移动网络环境中信息推送不可靠、推送方式和推送信息类型单一等问题,在传统基于固定网络的信息推送技术的基础上,开发了一套适合移动网络的混合型信息推送系统。结合用户身份信息、用户订阅的主题、用户所属的任务信息、用户所在位置和通过数据挖掘获取的用户潜在兴趣等信息,对系统中的信息进行过滤,并将过滤结果推送到客户端。允许管理人员选择推送方式,支持多种信息类型推送,并对推送结果进行反向跟踪,从而有效满足了移动环境下信息推送的需求,在实际的应用中也取得了很好的使用效果。     

基于微信的移动化数字校园信息平台设计与实现

为满足当前高职院校师生对数字校园信息平台移动化的迫切需求,在分析高职师生移动信息处理习惯的基础上,结合当前主流移动化开发技术,通过对现有数字化校园平台的移动化改造,提升平台的移动使用体验,使广大师生更加便利地使用数字化校园平台。分析了用户需求,构建了平台功能架构,利用ThinkPHP开发框架实现了该系统。结果表明:师生使用数字校园信息平台意愿和频率明显提升、各类教育教学数据得到极大丰富,系统具有比较强的可用性。