3PRS: a personalized popular program recommendation system for digital TV for P2P social networks

Digital TV channels require users to spend more time to choose their favorite TV programs. Electronic Program Guides (EPG) cannot be used to find popular TV programs. Hence, this paper proposes a personalized Digital Video Broadcasting — Terrestrial(DVB-T) Digital TV program recommendation system for P2P social networks. From the DVB-T signal, we obtain EPG of TV programs. The frequency and duration of the programs that users have watched are used to extract programs that users are interested in. The information is collected and weighted by Information Retrieval (IR). The program information is then clustered by k-means. Clusters of users are also grouped by k-means to find cluster relationships. In each group, we decide the most popular program in the group according to the program weight of the channel. When a new user begins to watch the TV program, the K-Nearest Neighbor (kNN) classification method is used to determine the user’s predicted cluster label. Then, our system recommends popular programs in the predicted cluster and similar clusters.     

Mass personalization: social and interactive applications using sound-track identification

This paper describes mass personalization, a framework for combining mass media with a highly personalized Web-based experience. We introduce four applications for mass personalization: personalized content layers, ad hoc social communities, real-time popularity ratings and virtual media library services. Using the ambient audio originating from a television, the four applications are available with no more effort than simple television channel surfing. Our audio identification system does not use dedicated interactive TV hardware and does not compromise the user’s privacy. Feasibility tests of the proposed applications are provided both with controlled conversational interference and with “living-room” evaluations.     

Neural TV program recommendation with label and user dual attention

TV program recommendation is very important for users to find interesting TV programs and avoid confusing users with a lot of information. Currently, they are basically traditional collaborative filtering algorithms, which only recommend through the interactive data between users and programs ignoring the important value of some auxiliary information. In addition, the neural network method based on attention mechanism can well capture the relationship between program labels to obtain accurate program and user representations. In this paper, we propose a neural TV program recommendation with label and user dual attention (NPR-LUA), which can focus on auxiliary information in program and user modules. In the program encoder module, we learn the auxiliary information from program labels through neural networks and word attention to identify important program labels. In the user encoder module, we learn the user representation through the programs that the user watches and use personalized attention mechanism to distinguish the importance of programs for each user. Experiments on real data sets show that our method can effectively improve the effectiveness of TV program recommendations than other existing models.

     

Information diffusion in distributed OSN: The impact of trusted relationships

Distributed Online Social Networks (DOSN) are a valid alternative to OSN based on peer-to-peer communications. Without centralised data management, DOSN must provide the users with higher level of control over their personal information and privacy. Thus, users may wish to restrict their personal network to a limited set of peers, depending on the level of trust with them. This means that the effective social network (used for information exchange) may be a subset of the complete social network, and may present different structural patterns, which could limit information diffusion. In this paper, we estimate the capability of DOSN to diffuse content based on trust between social peers. To have a realistic representation of a OSN friendship graph, we consider a large-scale Facebook network, from which we estimate the trust level between friends. Then, we consider only social links above a certain threshold of trust, and we analyse the potential capability of the resulting graph to spread information through several structural indices. We test four possible thresholds, coinciding with the definition of personal social circles derived from sociology and anthropology. The results show that limiting the network to “active social contacts” leads to a graph with high network connectivity, where the nodes are still well-connected to each other, thus information can potentially cover a large number of nodes with respect to the original graph. On the other hand, the coverage drops for more restrictive assumptions. Nevertheless the re-insertion of a single excluded friend for each user is sufficient to obtain good coverage (i.e., always higher than 40 %) even in the most restricted graphs. We also analyse the potential capability of the network to spread information (i.e., network spreadability), studying the properties of the social paths between any pairs of users in the graph, which represent the effective channels traversed by information. The value of contact frequency between pairs of users determines a decay of trust along the path (the higher the contact frequency the lower the decay), and a consequent decay in the level of trustworthiness of information traversing the path. We show that selecting the link to re-insert in the network with probability proportional to its level of trust is the best re-insertion strategy, as it leads to the best connectivity/spreadability combination.     

An intelligent subtitle detection model for locating television commercials.

A strategy for locating television (TV) commercials in TV programs is proposed. Based on the observation that most TV commercials do not have subtitles, the first stage exploits six subtitle constraints and an adaptive neurofuzzy inference system model to determine whether a frame contains a subtitle or not. The second stage involves locating the mark-in/mark-out points using a genetic algorithm. An interactive user interface allows users to efficiently identify and fine-tune the exact boundaries separating the commercials from the program content. Furthermore, erroneous boundaries are manually corrected. Experimental results show that the precision rate and recall rates exceed 90%.     

基于用户信任和兴趣的概率矩阵分解推荐方法

传统协同过滤推荐算法存在数据稀疏性、冷启动、新用户等问题.随着社交网络和电子商务的迅猛发展,利用用户间的信任关系和用户兴趣提供个性化推荐成为研究的热点.本文提出一种结合用户信任和兴趣的概率矩阵分解（STUIPMF）推荐方法.该方法首先从用户评分角度挖掘用户间的隐性信任关系和潜在兴趣标签,然后利用概率矩阵分解模型对用户评分信息、用户信任关系、用户兴趣标签信息进行矩阵分解,进一步挖掘用户潜在特征,缓解数据稀疏性.在Epinions数据集上进行实验验证,结果表明,该方法能够在一定程度上提高推荐精度,缓解冷启动和新用户问题,同时具有较好的可扩展性.     

Annotation alignment method on P2P-TV system

Peer-to-Peer Networking and Applications - P2P-TV is a TV system that receives content through a peer-to-peer network. Content is stored in the distributed manner then to be serviced to users, and...     

基于用户兴趣和社交信任的聚类推荐算法

个性化推荐系统中使用最广泛的算法是协同过滤算法,针对该算法存在的数据稀疏和扩展性差问题,提出了一种基于用户兴趣和社交信任的聚类推荐算法。该算法首先基于聚类技术根据用户评分信息将具有相同兴趣的用户聚为一类,并建立基于用户兴趣相近的邻居集合。为了提高兴趣相似度计算的准确性,采用了修正余弦计算公式来消除评分标准的差异问题。然后,引入信任机制,通过定义直接信任、间接信任、传递路径和计算方法来度量社交网络用户之间隐含的信任值,将社交网络转换为信任网络,依据信任程度来创建基于社交信任的邻居集合。通过加权的方式将基于两种邻居集合的预测值融合起来为用户产生项目的推荐。在Douban数据集上进行仿真实验,确定了最优的协调因子值和分类数值,并与基于用户的协同过滤算法和基于信任的推荐算法进行对比,实验结果表明,所提算法的平均绝对误差(MAE)减少了6.7%,准确率(precision)、覆盖(recall)和F1值分别增加了25%、40%和37%,有效提高了推荐系统的推荐质量。     

A novel prebuffering scheme for IPTV service

The Internet Protocol TeleVision (IPTV) network utilizes the broadband IP network to provide users the TV service. Due to the limited bandwidth of the IP network, IPTV does not broadcast the content of all channels to its users. The channel zapping time (i.e., the delay between the time when the user switches to a new TV channel and the time when the content of the new TV channel is received by the user) and content miss (i.e., the content of the new TV channel arrives after the user switches to another TV channel so that the user did not watch the content of the new TV channel) significantly affect the QoS of IPTV service. This paper proposes Dynamic Prebuffering Scheme (DPS) that dynamically prebuffers the content of TV channels in the Home Gateways (HGs) based on the user’s behavior to reduce the channel zapping time and the content miss probability. A prebuffer timer is implemented in DPS to reduce the bandwidth consumption. Both the analytic model and simulation experiments are developed to investigate the performance of DPS. The simulation results show that the performance enhancements are bounded by the setup of the number of prebuffering channels and the prebuffer timer. Based on the performance study, the IPTV operators can properly set the number of prebuffered channels and the prebuffer timer to obtain good performance. Our study indicates that DPS can significantly reduce the channel zapping time and the content miss probability by slightly increasing bandwidth consumption.     

Aligning principal and agent’s incentives: A principal–agent perspective of social networking sites

The viability of networked communities depends on the creation and disclosure of user-generated content and the frequency of user visitation (Facebook 10-K Annual Report, 2012). However, little is known about how to align the interests of user and social networking sites. In this study, we draw upon the principal-agent perspective to extend Pavlou et al.’s uncertainty mitigation model of online exchange relationships (2007) and propose an empirically tested model for aligning the incentives of the principal (user) and the agent (service provider). As suggested by Pavlou et al., we incorporated a multi-dimensional measure of trust: trust of provider and trust of members. The proposed model is empirically tested with survey data from 305 adults aged 20-55. The results support our model, delineating how real individuals with bounded rationality actually make decision about information disclosure under uncertainty in the social networking site context. There is show little to no relationship between online privacy concerns and information disclosure on online social network sites. Perceived benefits provide the linkage between the incentives of principal (user) and agent (provider) while usage intensity demonstrated the most significant impact on information disclosure. We argue that the phenomenon may be explained through Communication Privacy Management Theory. The present study enhances our understanding of agency theory and human judgment theory in the context of social media. Practical implications for understanding and facilitating online social exchange relationships are also discussed.     

VectorTrust: trust vector aggregation scheme for trust management in peer-to-peer networks

With emerging Internet-scale open content and resource sharing, social networks, and complex cyber-physical systems, trust issues become prominent. Conventional trust mechanisms are inadequate at addressing trust issues in decentralized open environments. In this paper, we propose a trust vector based trust management scheme called VectorTrust for aggregation of distributed trust scores. Leveraging a Bellman–Ford based algorithm for fast and lightweight trust score aggregation, VectorTrust features localized and distributed concurrent communication. Built on a trust overlay network in a peer-to-peer network, a VectorTrust-enabled system is decentralized by nature and does not rely on any centralized server or centralized trust aggregation. We design, implement, and analyze trust rating, trust aggregation, and trust management strategies. To evaluate the performance, we design and implement a VectorTrust simulator (VTSim) in an unstructured P2P network. The analysis and simulation results demonstrate the efficiency, accuracy, scalability, and robustness of VectorTrust scheme. On average, VectorTrust converges faster and involves less complexity than most existing trust schemes. VectorTrust remains robust and tolerant to malicious peers and malicious behaviors. With dynamic growth of P2P network scales and topology complexities, VectorTrust scales well with reasonable overheads (about O(lg?N) communication overheads) and fast convergence speed (about O(log? _D N) iterations).     

基于物品的改进协同过滤算法及应用

针对电视产品信息资源量过载导致用户选择困难的问题,本文主要研究了基于物品的协同过滤算法在电视产品推荐系统中的改进及应用,将个性化推荐技术和电视产品系统有机结合来满足用户和运营商的需求.在推荐过程中,首先收集用户的偏好建立数据模型,以用户观看电视产品的时长作为用户偏好的显式特征,然后在传统的协同过滤算法中引入点播金额权重进行改进,并采用欧几里德距离法计算物品相似度,最后根据邻居集合预测目标用户对电视产品的观看时长,得到推荐结果.实验表明,通过引入点播金额权重这一改进能够提高推荐的准确性.     

一种基于组群的P2P网络信任模型*

由于P2P网络的开放、匿名等特点,使得P2P网络对节点缺乏约束机制,节点间缺乏信任。提出了一种应用于非结构化P2P网络的信任模型——BGTrust。该模型对组群内信任采用局部推荐信任和组群间信任采取全局信任的方法进行处理,充分结合了全局信任和局部信任的优点。仿真表明,该信任模型在对交互的信任度评价可信度和抑制恶意节点方面较已有模型有一定改进。     

A trust evaluation scheme for complex links in a social network: a link strength perspective

Potential security threats pose a significant challenge to evaluating the trustworthiness of complex links among users in a social network. Traditional trust computation methods typically consider user comments or interaction, thereby reflecting the trustworthiness between users according to their past experiences. However, the tie strength, which reflects the closeness of user relationships, is also a potential factor for estimating the trustworthiness of links among users. To incorporate this indicator, we propose a trust evaluation scheme for complex links comprising two aspects: the reliability and strength of links among uses. Our main contributions are (1) a trust calculation method, including direct trust for directly linked users and indirect trust for indirectly linked users, which is established based on the comment factor, forwarding factor, and approving factor; (2) a link strength evaluation method to determine the trustworthiness of direct and indirect links between users considering comment stability, mutual trust, interaction frequency, and common neighbours and community similarity, and (3) a link trust evaluation algorithm based on the link trust matrix synthesizing the reliability and strength of links. The experimental results and analysis show that our proposed scheme is feasible and effective in improving the performance of trust evaluation in a social network.     

用户行为驱动的网页布局自动调整的研究

在传统的Web网站中,网页的布局往往由网页制作人员安排并很少变化.为了更好的为网络用户提供服务,提出通过对Web日志的数据清洗,识别出每个用户在一个会话期内访问的页面,依据网页内客在逻辑上的关系和用户经常访问的页面,得到用户对网页内容的兴趣度矩阵及各子项目的兴趣度矩阵.对网络用户根据兴趣度短阵进行层次化的分类,得到每个...     

A DVB-MHP web browser to pursue convergence between Digital Terrestrial Television and Internet

In the last decade with the growth of Interactive Digital Television (IDTV) we have seen the end of passive television. An example of this trend is Internet access through television by means of the last generation Set Top Boxes (STBs). The chance to enjoy web contents through digital television Set Top Boxes, delivering a satisfying browsing experience across this platform, could provide the opportunity to promote social inclusion and bridging the “digital divide”. In this paper we present WebClimb, a web browser that would pursue an effective integration of Digital Terrestrial Television (DTT) and Internet in the DVB-MHP platform. WebClimb is a Java-based web browser that enables users to browse the web by interacting with an easy to use Graphical User Interface (GUI), driven by a common TV remote control without asking for reformatting such a content on the server side. In addition to this, the main requirement has been to design and develop an MHP browser application to be broadcast through a TV channel and not embedded in a specific device, though it could be too. Experimental results and a comparison with other possible solutions are provided.     

一种融合信任和项目卷积描述信息的PMF算法

针对现有概率矩阵分解(PMF)技术的个性化推荐系统在采用社交网络中信任信息时常常忽视项目相关描述文档信息的问题,提出一种融合用户信任和通过卷积网络以获取项目描述等信息的PMF模型.首先,利用用户偏好信息和行为轨迹信息构建一种新的信任网络;然后,通过卷积神经网络从项目描述文档中提取项目潜在的特征向量;最后,在概率矩阵分解过程中同时利用评分数据、信任网络中用户的信任信息和项目的描述信息,计算用户和项目的潜在特征向量以预测评分并进行个性化推荐.为验证算法的有效性,选择3种算法在4个数据集上进行对比,实验结果表明所提出的算法在推荐精确度和鲁棒性方面优于其他3种算法.     

Multi-relational reinforcement for computing credibility of nodes

Nowadays, growing number of social networks are available on the internet, with which users can conveniently make friends, share information, and exchange ideas with each other. As the result, large amount of data are generated from activities of those users. Such data are regarded as valuable resources to support different mining tasks, such as predicting friends for a user, ranking users in terms of their influence on the social network, or identifying communities with common interests. Traditional algorithms for those tasks are often designed under the assumption that a user selects another user as his friend based on their common interests. As a matter of fact, users on a social network may not always develop their friends with common interest. For example, a user may randomly select other users as his friends just in order to attract more links reversely from them. Therefore, such links may not indicate his influence. In this paper, we study the user rank problem in terms of their ‘real’ influences. For this sake, common interest relationships among users are established besides their friend relationships. Then, the credible trust link from one node to another is on account of their similarities, which means the more similar the two users, the more credible their trust relation. So the credibility of a node is high if its trust inlinks are credible enough. In this work, we propose a framework that computes the credibility of nodes on a multi-relational network using reinforcement techniques. To the best of our knowledge, this is the first work to assess credibility exploited knowledge on multi-relational social networks. The experimental results on real data sets show that our framework is effective.     

P2P流媒体技术飞速发展

P2P流媒体技术,也称为对等网络(peer-to-peer)技术,简单地说,就是一种用户不经过中继设备直接交换数据或服务的技术。它将目前互联网的"内容位于中心"模式改变为"内容位于边缘"模式,将权利交还给用户。在这种架构中,每个节点的地位都相同,具备客户端和服务器的双重特性,可以同时作为服务使用者和服务提供者。该文将为读者讲述在P2P流媒体技术的原理以及应用,希望能使大家对这种现阶段应用广泛,发展迅速的流媒体技术有一定的了解。     

Providing awareness,explanation and control of personalized filtering in a social networking site

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system.