基于用户兴趣和评分差异的改进混合推荐算法

为进一步提高协同过滤推荐算法的推荐精度,提出一种基于用户兴趣和评分差异的改进混合推荐算法.利用词频-逆向文件频率(TF-IDF)思想对稀疏矩阵进行填充,在计算用户相似度时在传统的修正余弦相似度计算公式中引入两个不同的影响因子来改善用户评分差异的影响,使用两种不同的时间衰减函数用于修正时间因素对用户和项目之间以及用户与用户之间的影响.实验结果表明,该算法能够缓解数据稀疏的问题,有效修正用户评分差异和用户兴趣变化对推荐结果的影响,其推荐精度均优于现有其它改进算法.     

Mobile computing: a user study on hedonic/utilitarian mobile device usage

Intrinsic motivators of technology beliefs have received scant attention in the technology acceptance literature despite indications of their efficacy. This study uses the framework of TAM to explore the effect of intrinsic variables on technology beliefs and user behavior. Specifically, we examine the effect of cognitive absorption and playfulness on user beliefs including perceived enjoyment and perceived usefulness within the context of mobile devices. Moreover, we manipulate the hedonic and utilitarian purpose of the mobile device to determine how the nature of the device influences user beliefs. Findings indicate that cognitive absorption and user playfulness significantly impact beliefs and that the hedonic or utilitarian orientation of the technology has implications for maximizing use.     

Exploring the influence of reference situations and reference pricing on mobile service user behaviour

Despite widespread proliferation of mobile devices providing access to a variety of advanced and data-rich services, adoption of those services remains low in most of the Western world. Thus, research related to the acceptance and use of mobile technology and services continues to develop. Traditional research in this domain has been useful for exploring adoption and use related to individual technologies or novel services. However, our research efforts indicate that users often reflect on former experiences with similar technologies or services when choosing mobile services. This suggests that when exploring mobile service usage decisions, focus cannot be placed on mobile services alone. Through two longitudinal field studies conducted in Denmark during 2004 and 2005, this article further explores the manner in which users' choices of mobile services are influenced by their reference situations and reference prices. The article suggests that users cognitive referencing is an influential factor that must be considered when exploring their usage behaviour in the mobile services market.     

Developing data allocation schemes by incremental mining of user moving patterns in a mobile computing system

In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two schemes are devised: one utilizes the set level of moving patterns and the other utilizes their path level. Schemes for shared data are also developed. Performance of these schemes is comparatively analyzed.     

基于Mahout的新用户推荐算法的设计与实现

为了解决大数据背景下新用户因没有历史数据而导致推荐难和推荐效率低等问题,提出将基于Mahout的协同过滤算法与基于MapReduce的Top N算法相结合的技术方法,来实现新用户推荐算法,从而构建新用户推荐系统的架构,并对Hadoop Top N算法以及Mahout中协同过滤算法进行设计与实现。理论分析和实验验证表明,该新用户推荐算法在推荐效率、对大规模数据处理的伸缩性以及推荐质量上都明显优于单独使用协同过滤算法的新用户推荐。     

基于社交网络和关联数据的服务网络构建方法

网络中可用服务的急剧增加对面向服务计算技术的发展起到了极大的推动作用。针对服务的规模和利用率远没有达到预期,以及服务之间交互关系的复杂性问题,提出基于社交网络和关联数据的服务网络构建方法。首先,结合社交网络和关联数据方法提出服务网络（SN）的概念,以提高服务的规模及利用率;其次,为了提升服务发现的可行性及有效性,构建服务网络中的服务社区;然后,形式化地表述服务网络中服务之间的关联关系和属性,以解决服务之间互操作问题;最后,结合旅游服务网络案例研究分析所提出的方法。研究结果表明,基于社交网络和关联数据的方法可有效解决网络服务利用率低以及服务关系复杂性的问题。     

Evaluating mobile app usage by service sector micro and small enterprises in Nigeria: an abductive approach

ABSTRACT

This research investigated how mobile apps influence the dynamic capabilities of service sector micro and small enterprises (MSEs) in Lagos, Nigeria. Using an abductive method, data from 388 service sector MSEs was examined through exploratory factor analysis. The resultant model suggests that mobile app usage barely increases the absorptive capability (integrating new learning into the organization) of MSEs; rather, it strongly influences the ability to seize opportunities. The result implies that mobile app usage by service sector MSEs in Lagos deviates from the conventional views on the micro foundations of the dynamic capability framework, which argues that sensed opportunities are first analysed (shaped) before resources are deployed towards their maximization. These findings suggest that the service sector MSEs in Lagos seldom scrutinize opportunities before deploying resources to seize them. This study extends IS literature on how mobile apps help MSEs to exploit business opportunities in resource-constrained contexts.     

Building a validation measure for activity-based transportation models based on mobile phone data

Activity-based micro-simulation transportation models typically predict 24-h activity-travel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure.Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models.By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.     

Towards a framework for large-scale multimedia data storage and processing on Hadoop platform

Cloud computing techniques take the form of distributed computing by utilizing multiple computers to execute computing simultaneously on the service side. To process the increasing quantity of multimedia data, numerous large-scale multimedia data storage computing techniques in the cloud computing have been developed. Of all the techniques, Hadoop plays a key role in the cloud computing. Hadoop, a computing cluster formed by low-priced hardware, can conduct the parallel computing of petabytes of multimedia data. Hadoop features high-reliability, high-efficiency, and high-scalability. The numerous large-scale multimedia data computing techniques include not only the key core techniques, Hadoop and MapReduce, but also the data collection techniques, such as File Transfer Protocol and Flume. In addition, distributed system configuration allocation, automatic installation, and monitoring platform building and management techniques are all included. As a result, only with the integration of all the techniques, a reliable large-scale multimedia data platform can be offered. In this paper, we introduce how cloud computing can make a breakthrough by proposing a multimedia social network dataset on Hadoop platform and implementing a prototype version. Detailed specifications and design issues are discussed as well. An important finding of this article is that we can save more time if we conduct the multimedia social networking analysis using Cloud Hadoop Platform rather than using a single computer. The advantages of cloud computing over the traditional data processing practices are fully demonstrated in this article. The applicable framework designs and the tools available for the large-scale data processing are also proposed. We show the experimental multimedia data including data sizes and processing time.     

Facilitating data exploration with query previews: a study of user performance and preference

Networked and local data exploration systems that use command languages, menus, or form fill-in interfaces rarely give users an indication of the distribution of data. This often leads users to waste time, posing queries that have zero-hit or mega-hit results. Query previews are a novel visual approach for browsing databases. Query previews supply users with data distribution information for selected attributes of the database, and give continuous feedback about the size of the result set as the query is being formed. Subsequent refinements might be necessary to narrow the search. As there is a risk that query previews are an additional step, leading to a more complex and slow search process, a within-subjects empirical study was ran with 12 subjects who used interfaces with and without query previews and with minimized network delays. Even with 12 subjects and minimized network delays statistically significant differences were found, showing that query previews could speed up performance 1.6 to 2.1 times and lead to higher user satisfaction.     

RPR: recommendation for passengers by roads based on cloud computing and taxis traces data

Traffic jam has grown to be a more and more difficult problem to be solved in big cities around the world, and people are getting less chance to take taxis. In order to remit this issue, we propose a recommendation strategy based on taxi traces data for passenger by the roads to make it easier. Considering the scale of taxi traces data, Hadoop is employed to handle the traces data, whose tasks include filtering and cleaning of the data, mapping taxi traces, and computing the average passage time and empty taxi arrival rate on the roads. When a user uploads his position and the time, assisted by weather condition gain from the Internet, we get the very model that corresponds to the date and the weather; the time interval is thought to be the expected waiting time between the moment when user requests the service and the moment when the cumulative number of empty taxi is greater than or equal to 1 after adding the time the taxi spends on the road, and is pushed to the user. The experiment is conducted on the base of a real-world dataset generated by over 12,000 taxis over a period of 3 months in Beijing. Experimental results demonstrate that the processing speed of Hadoop is nine times faster than serial’s, which displays the feasibility of Hadoop in the application of massive traces data. In addition, the accuracy of the recommendation strategy reaches up to 88.75 %, and it meets the demand of real-time service.     

Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter

Accurate estimates of mobile robot location, if available, can be used to improve the performance of a vehicle dynamics control system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter, providing robot position and orientation estimates at each sampling instant. To cope with the tracking of long trajectories, the performance of the filter is improved by introducing an on-line fuzzy-rule-based adaptation scheme.     

Region with velocity constraints: map information and its usage for safe motion planning of a mobile robot in a public environment

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.     

面向服务的物联汇聚平台的研究与开发

针对离散制造业现场设备层与上层应用系统(如ERP,MES,PLM系统)之间信息流不顺畅、数据结构异构、信息中转次数多、系统集成复杂度高的问题,提出了一种面向服务的物联汇聚平台,采用OPC UA技术使工业异构系统互联,为智能制造体系提供工业物联网平台系统解决方案。设计了灵活多样的驱动模块解决现场设备及系统异构问题,基于OPC UA地址空间的数据组态模块解决数据结构不统一的问题,最终实现数据及文件的跨层传输,简化系统集成复杂度。通过OPC UA客户端程序与Modbus模拟器程序测试,系统运行良好。     

The effects of zoomable user interfaces and user age in searching for a target with a mouse on a two-dimensional information space

A zoomable user interface (ZUI) is a useful function to help users deal with large information spaces displayed within a screen. Although used in many applications, ZUIs have not been sufficiently studied in terms of usability. Usability problems may be more crucial for older people than younger ones. The objective of this study is to examine the effects of a combination of two zoom factors (three zoom focus methods: original-center, re-center, and aiming-point; and two zoom scales: +100% and +400%) for two age groups (younger and older adults) on mouse-based information searching tasks in a map-type two-dimensional information space. Twenty four volunteers (twelve users per age group) participated in the experiments by answering a pair of questions about fifty movie information topics. Task completion time, number of operations, and number of errors were selected as performance measures, and a subjective assessment of satisfaction was collected. Older adults used the tested ZUIs less efficiently and precisely than younger adults. The effects of zoom focus methods vary with zoom scale levels regardless of the age groups. The participants also preferred using the aiming-point focus regardless of zoom scale and using the re-center focus with the +400% scale. We discuss potential implications of the age-related performance differences and the effects of ZUI functions, and suggest some ZUI design guidelines in conclusion. We expect that the findings can be used as basic resources in designing various web services and applications for older computer users.

Relevance to industry

Given that a zoomable user interface is widely used in basic geographic information systems, web services, and various applications, using a selective-focus method such as the aiming-point focus can improve computer users’ usability; in particular, the zooming functions will greatly help older people to explore large information spaces quickly and easily.