The media-oriented cross domain recommendation method

Multimedia Tools and Applications - With the rapid development of modern high-tech, such as big data and artificial intelligence, the demand for cross-media services is also greatly improved. Since...     

基于可变Cookie的跨域单点登录

针对单点登录中的跨域身份认证问题,提出了一种基于可变Cookie的方案解决跨域单点登录,使用随机数字生成票据,并作为传统加密算法的会话密钥对客户端的Cookie进行加密,采用现代加密算法在异域系统之间安全传递票据,每次认证产生新的票据并更新异域应用系统的Cookie。通过对票据产生和传输以及Cookie加密和常见攻击的安全性分析,可以实现跨域单点登录的功能并保证身份认证安全可信。     

Fractional Fourier domain equalization for single carrier broadband wireless systems

In this paper,a novel single carrier equalization approach in the fractional Fourier domain(FRFD) is proposed.It can remove all the inter-symbol interference(ISI) and avoid the considerable noise enhancement of the frequency domain-zero forcing(FD-ZF) equalizer.As the fractional Fourier transform makes a chirp spread,the impulse response of the deep fading channel may be flattened in some orders of the FRFD while it would be greatly attenuated in the FD.By searching for an optimal order under certain criterion,we take advantages of the ZF algorithm to mitigate the effects of the ISI completely.This approach can overcome the contradiction between the ISI mitigation and the noise enhancement of the FD-ZF equalizer.Theoretical analysis and simulation results show that the proposed FRFD-ZF equalizer can achieve a significant performance and have the same computation cost O(N log N) as the conventional FD linear equalizer,especially in the frequencyselective deep-fading channels.     

Adapting domain ontology for personalized knowledge search and recommendation

This paper presents research on the development of a domain ontology adaptation system for personalized knowledge search and recommendation that adapts a suitable domain ontology according to the previous browsing and reading behavior of users (i.e., usage history log). An adaptive domain ontology can satisfy the future requirements of users and promote use value. In developing the system, a domain ontology adaptation model is first designed. Based on the designed adaptation model, a methodology for domain ontology adaptation is developed. Subsequently, a domain ontology adaptation system is implemented with an illustrative example of securities trading. Finally, a system evaluation for user satisfaction and a methodology evaluation are conducted to demonstrate that the developed methodology and system worked efficiently.     

Ubiquitous recommendation systems

In many popular visions of ubiquitous computing, the environment proactively responds to individuals who inhabit the space. For example, a display magically presents a personalized advertisement, the most relevant video feed, or the desired page from a secret government document. Such capability requires more than an abundance of networked displays, devices, and sensors; it relies implicitly on recommendation systems that either directly serve the end user or provide critical services to some other application. As recommendation systems evolve to exploit new advances in ubiquitous computing technology, researchers and practitioners from technical and social science disciplines must collaborate to address the challenges to their effective implementation. Although it may be impossible to perfectly anticipate each individual's needs at any place or time, ubiquitous computing will enable such systems to help people cope with an expanding array of choices.     

Decision feedback data detection in frequency domain for single carrier cyclic prefix assisted CDMA systems: A sequential quadratic programming approach

This paper proposes a frequency domain decision feedback data receiver for the uplink transmission of broadband single carrier cyclic prefix-assisted CDMA systems. The optimum data detection problem based on the maximal-likelihood criteria for this system is addressed as a combinatorial optimization problem. A sequential quadratic programming approach is proposed to solve this problem. The parallel gradient projection method and projected successive over relaxation algorithm are proposed to solve the sequential quadratic programming problem, which corresponds to low-complexity nonlinear parallel and successive interference cancellation schemes in frequency domain. The convergence properties and the complexities of these methods are analyzed and compared with conventional methods. The simulation results show that, with a few iterations, the proposed scheme gives near single user performance even for fully loaded systems.     

Social group recommendation in the tourism domain

Recommender Systems learn users’ preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual users. In this article, we present a social based approach for recommender systems in the tourism domain, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members of a group. This aspect is a hot research topic in the recommender systems area. In addition, to generate the individual and group recommendations our approach uses a hybrid technique that combines three well-known filtering techniques: collaborative, content-based and demographic filtering. In this way, the disadvantages of one technique are overcome by the others. Our approach was materialized in a recommender system named Hermes, which suggests tourist attractions to both individuals and groups of users. We have obtained promising results when comparing our approach with classic approaches to generate recommendations to individual users and groups. These results suggest that considering the type of users’ relationship to provide recommendations to groups leads to more accurate recommendations in the tourism domain. These findings can be helpful for recommender systems developers and for researchers in this area.     

一种改进的缺失数据协同过滤推荐算法

协同过滤推荐算法是推荐系统研究的热点,近年来,在亚马逊、淘宝等商业系统中获得应用。在实际应用过程中,协同过滤推荐面临数据稀疏和准确性低的问题。作为推荐基础的用户-产品(项目)矩阵通常非常稀疏(存在大量缺失数据),从而导致推荐结果不准确。文章试图在缺失数据情况下提高协同过滤推荐的准确性,聚焦以下两个方面:(1)用户相似度、产品(项目)相似度计算;(2)缺失数据预测。首先,用增强的皮尔森相关系数算法,通过增加参数,对相似度进行修正,提高用户、产品(项目)相似度计算的准确率。接着,提出一种同时考虑了用户和产品(项目)特征的缺失数据预测算法。算法中,对用户和产品(项目)分别设置相似度阈值,只有当用户或产品(项目)相似度达到阈值时,才进行缺失数据预测。预测过程中,同时使用用户和产品(项目)相似度信息,以提高准确度。在模型基础上,用淘宝移动客户端的数据集进行了验证,实验结果表明所提算法比其他推荐算法要优异,对数据稀疏性的鲁棒性要高。     

A weight-based item recommendation approach for electronic commerce systems

The recommendation system is a useful tool that can be employed to identify potential relationships between items and users in electronic commerce systems. Consequently, it can remarkably improve the efficiency of a business. The topic of how to enhance the accuracy of a recommendation has attracted much attention by researchers over the past decade. As such, many methods to accomplish this task have been introduced. However, more complex calculations are normally necessary to achieve a higher accuracy, which is not suitable for a real-time system. Hence, in this paper, we propose a weight-based item recommendation approach to provide a balanced formula between the recommended accuracy and the computational complexity. The proposed methods employ a newly defined distance to describe the relationship between the users and the items, after which the recommendations and predictive algorithms are developed. A data analysis based on the MovieLens datasets indicates that the methods applied can obtain suitable prediction accuracy and maintain a relatively low computational complexity.     

电子商务推荐系统中推荐策略的自适应性

针对电子商务推荐系统中各种推荐技术的不足,提出推荐策略的自适应方法。用二元组《用户知识,推荐商品》代表推荐环境的根本特征．采用ART神经网络进行自学习,获取推荐环境的不同聚类。每个聚类代表了某种推荐环境,对推荐结果的反馈情况进行统计分析．确定每个聚类的最佳推荐技术。向用户推荐商品时,根据用户所在聚类采用具有最佳推荐质量的推荐技术向用户作出推荐。整个系统的工作过程不需要人工干预,具有自适应性。     

Hybrid followee recommendation in microblogging systems

Bug reports are widely employed to facilitate software tasks in software maintenance. Since bug reports are contributed by people, the authorship characteristics of contributors may heavily impact the perfor-mance of resolving software tasks. Poorly written bug reports may delay developers when fixing bugs. However, no in-depth investigation has been conducted over the authorship characteristics. In this study, we first leverage byte-level N-grams to model the authorship characteristics and employ Normalized Simplified Profile Intersection (NSPI) to identify the similarity of the authorship characteristics. Then, we investigate a series of properties related to contributors’ authorship characteristics, including the evolvement over time and the variation among distinct products in open source projects. Moreover, we show how to leverage the authorship characteristics to facilitate a well-known task in software maintenance, namely Bug Report Summarization (BRS). Experiments on open source projects validate that incorporating the authorship characteristics can effectively improve a state-of-the-art method in BRS. Our findings suggest that contributors should retain stable authorship characteristics and the authorship characteristics can assist in resolving software tasks.

     

基于知识的推荐系统综述

基于知识的推荐算法在弥补过滤式传统推荐算法冷启动、数据缺失、"信息茧房"等问题上发挥着重要的作用.对基于知识的推荐算法相关研究进行评述,探讨其研究现状及研究进展.通过梳理CNKI相关文献和WOS中2017-2020的论文,采用内容分析法对国内外学术界基于知识的推荐算法的研究进展进行分析,对实际进展包括会话过程的优化、领...     

A survey on personality-aware recommendation systems

With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.

     

STS:跨域安全令牌交换机制的研究和实现

由于分布式环境下的网格应用程序往往要跨域访问资源,如何构建统一的跨域安全令牌交换机制就成了网格应用中需解决的一个重要的问题.针对此问题,提出了一个可以进行跨域安全令牌交换的框架,此框架由认证中心和个体安全域组成,单个安全域的用户可以通过认证中心来获取不同安全域的安全令牌,从而为跨域安全令牌的交换提出了一种解决方法,最后框架在欧中网格合作项目(Bridge项目)中进行验证.     

Applying artificial immune systems to collaborative filtering for movie recommendation

Collaborative filtering is a widely used recommendation technique and many collaborative filtering techniques have been developed, each with its own merits and drawbacks. In this study, we apply an artificial immune network to collaborative filtering for movie recommendation. We propose new formulas in calculating the affinity between an antigen and an antibody and the affinity of an antigen to an immune network. In addition, a modified similarity estimation formula based on the Pearson correlation coefficient is also developed. A series of experiments based on MovieLens and EachMovie datasets are conducted, and the results are very encouraging.     

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Applied Intelligence - Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the...     

融合用户属性信息的冷启动推荐算法

协同过滤算法广泛应用于个性化推荐系统中。现有的基于社群相似性的协同过滤算法在新用户新商品的冷启动场景中难以使用,性能较差。对此,提出了一种基于矩阵分解和神经网络映射的冷启动推荐算法。首先,使用矩阵分解方法求出用户在潜在兴趣空间的向量表示;然后,训练神经网络学习从用户属性数据到潜在兴趣向量的映射关系;最后,融合用户的历史评分数据与属性数据各自生成的兴趣向量,给出平滑的推荐预测值。实验表明,当用户的评分记录很少时,预测性能有明显提升,融合用户的属性信息能较好地改善"冷启动"情况下推荐系统的性能。