交互电视中基于本体的个性化节目协同推荐

提出一种在Web-TV环境中,拥有较强个性化和交互特性的基于本体的电视节目协同推荐方法。采用隐式和显式两种方法估计用户对其已收看节目的喜好程度,并根据用户收看电视节目的四条性质,提出隐式估计评分值的核心公式。在协同推荐时,利用节目本体中各元素的语义相似性,根据已经得到的评分值推测用户对未收看节目的评分值,解决了协同推荐的稀疏性缺点,并且在计算用户之间的相似度时,还考虑了用户的个人属性。最后还提出了实现了该算法的原型系统。     

基于模糊权重相似性的协同过滤算法研究

在传统协同过滤算法中,相似度直接依据用户评分。但是,用户评分会受各种不确定因素影响。采用数值评分的推荐系统收集到的用户喜好信息是模糊、不精确和不完整的。单一的数值不能包含丰富的信息来表达用户喜好,也会导致推荐结果的不准确性。文中定义了几种模糊集的隶属函数,提出了基于模糊逻辑的相似度计算方法。实验结果表明,基于模糊权重的相似度有效的提高了推荐系统的预测准确度,一定程度上解决了协同过滤算法的可扩展性和数据稀疏性问题。     

移动社会网络中基于位置的个性化餐馆推荐建模研究

移动网络中基于位置的服务(Location-Based Service,LBS)是用于移动设备的典型服务。文章总结了LBS推荐系统的主要特点及其应用,融合情景信息提出分阶段的LBS推荐系统模型。在冷启动阶段,根据用户历史数据基于规则推荐,预测新用户兴趣。当获得大量的历史用户反馈和交互数据,采用基于用户和情景的协同过滤推荐算法来提高推荐系统的精度,并修改规则库。实验表明该推荐模型能提高推荐精度并实时推荐。     

人工智能在广播电视节目推荐系统中的应用

介绍广播电视节目推荐系统的基本概念，重点讨论内容分析、协同过滤算法以及基于深度学习的推荐系统三种主要的人工智能技术在广播电视节目推荐系统中的应用，展示如何利用这些技术提供更精准的节目推荐，以提升用户体验，助力电视节目的精准推送和高效分发。     

自适应推荐算法在电子超市个性化服务系统中的应用研究

为了满足电子超市中用户的个性化的服务需求，提出并实现了一种基于支持向量机的自适应推荐算法。首先，将用户模型按照层次化方式组织成领域信息和原子需求信息，考虑多用户同类信息需求。采用支持向量机对领域信息节点中的原子需求信息进行分类协同推荐，然后在针对每一领域信息节点中的原子信息需求进行基于内容的过滤。该算法克服了分别采用协同推荐和基于内容的推荐单一方法的缺点，大大提高了信息的查准率和查全率，尤其适合大规模用户群的信息推荐。该算法用于基于电子超市的个性化推荐服务系统（PRSSES）中，结果表明是有效的。     

节目推荐系统的特性与应用

随着电视节目日益丰富，电视用户正面临与互联网用户类似的“信息过载”问题，如何帮助用户及时收看到所需的节目？节目推荐系统就可很好地解决这一问题。     

基于Spark平台的电子商务个性化信息推荐方法

互联网上数据传播量日益增加，但信息使用率却很低，消耗用户大量精力，针对这个问题，提出一种基于Spark平台的电子商务个性化信息推荐方法。Spark平台通过弹性分布式内存数据集，可将中间计算结果直接保存至内存中，建立用户喜好模型；使用评分机制计算不同个体偏好商品，形成推荐列表；引入挖掘隐含信息的矩阵分解算法，将未知参数转化为已知量，提高个性化信息推荐精准度。仿真对比实验，从用户满意度、信息熵值和运行速度三个角度，验证了所提方法可以实现优质且高效的电子商务个性化信息推荐工作。     

基于深度学习的混合兴趣点推荐算法

针对现有兴趣点推荐的初始化和忽视评论信息语义上下文信息的问题,将深度学习融入推荐系统中已经成为兴趣点推荐研究的热点之一。该文提出一种基于深度学习的混合兴趣点推荐模型(MFM-HNN)。该模型基于神经网络融合评论信息与用户签到信息来提高兴趣点推荐的性能。具体地,利用卷积神经网络学习评论信息的特征表示,利用降噪自动编码对用户签到信息进行初始化。进而,基于扩展的矩阵分解模型融合评论信息特征和用户签到信息的初始值进行兴趣点推荐。在真实签到数据集上进行实验,结果表明所提MFM-HNN模型相比其他先进的兴趣点推荐具有更好的推荐性能。     

个性化推荐系统在IPTV系统中的应用

IPTV丰富的多媒体业务能够较好地满足家庭用户多样化的收视需求。为了提升业务运营效果,提高用户黏性,IPTV需要实现“千人千面”的智慧运营。个性化推荐系统常被用于解决智慧运营中的信息过载问题,但同时也会受到数据稀疏性、冷启动以及可解释性等问题的困扰。本文介绍了基于节目知识图谱、图神经网络及点击率预估的个性化推荐系统,分析了它们在IPTV节目推荐系统的应用。借助三种新技术,将有助于挖掘用户的个性化需求,提高个性化推荐的推荐效果。     

基于上下文的智能应用推荐系统架构设计

普适计算环境需要根据用户和环境的上下文信息为用户提供丰富及合适的应用资源.为了适应这种需求,设计了一种基于上下文的智能应用推荐系统架构.该架构主要是利用贝叶斯网络根据上下文进行推理以实现应用推荐,同时使用EM(η)算法对推荐模型进行更新.实验证明该架构能为用户推荐合适的应用资源并可以随用户偏好的改变进行推荐模型更新.     

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.     

A Framework for Adaptive UbiComp Applications Based on the Jack-in-the-Net Architecture

The Jack-in-the-Net Architecture (Ja-Net) that we present in this paper provides a unique and promising approach to design ubiquitous computing applications that can scale, self-organize, and adapt to short- and long-term changes in network conditions and user preferences. In Ja-Net, network applications are implemented by a group of distributed, autonomous entities called the cyber-entities. Each cyber-entity implements a function component related to its service and follows simple behavior rules (such as migration, replication, energy exchange, death, and relationship establishment with other cyber-entities). They form organizations or communities by establishing and learning useful relationships with a number of other cyber-entities and collectively provide higher level services through interactions among them. Consequently, desirable services and characteristics emerge in network applications through autonomous and self-organizing interactions among cyber-entities (service emergence). In this paper, we discuss the design and implementation of Ja-Net platform software that achieves dynamic and adaptive provision of network applications through service emergence. We also built an application for Ja-Net that features service emergence and we empirically verified that the application can adapt itself to user preferences.     

一种基于用户偏好分析的查询优化方法

该文对如何满足不同兴趣用户的查询需求进行了研究,提出了一种基于用户偏好分析的查询优化方法。该方法将用户对网页的偏好转化为对本体知识库中实例的偏好;分析本体实例之间的语义关联,发现隐含的用户偏好;综合用户偏好历史,建立用户当前状态下偏好的数学模型,以预测用户对网页的关注程度。实现了相应的原型系统,实验结果表明,相对于传统的个性化搜索技术,该文提出的方法能更有效地获取用户偏好,适应用户偏好的变化,提高搜索引擎查询的准确率。     

基于置信用户偏好模型的电视推荐系统

基于用户偏好的电视节目个性化推荐是一种内容的推荐算法。其中用户偏好的不确定性和描述上的模糊性是用户模型建立的难点。在此首先通过对样本用户过往观看记录数据进行分析,发现用户偏好存在一定的时不变性。把偏好在一定时间内不发生变化的用户称作置信用户,在这个基础上,建立基于节目特征向量空间的用户偏好模型,并提出基于用户偏好度模型的推荐算法。该算法通过用户观看视频的历史记录得到用户的偏好模型,并基于该偏好模型向用户推荐节目。仿真实验证明了算法的收敛性和有效性。     

基于兴趣偏好的微博用户性别推断研究

用户属性,如：性别、年龄等,是计算心理学、个性化搜索、社会化商业推广等研究和应用考察的核心因素。利用用户生成数据自动推断用户属性成为新兴的研究课题。本文提出基于用户兴趣偏好研究微博用户的性别推断问题。考察了用户内容偏好以及关注行为偏好对性别推断的作用。在新浪微博近万名用户的数据集上证明了用户偏好特征的有效性。与传统的语用特征相比,将用户内容偏好与关注偏好相结合能够显著提高推断准确率。关注偏好特征对推断非活跃用户的性别尤其有效。     

An Adaptive User Preferences Elicitation Scheme for Location Recommendation

User preferences elicitation is a key issue of location recommendation.This paper proposes an adaptive user preferences elicitation scheme based on Collaborative filtering (CF) algorithm for location recommendation.In this scheme,user preferences are divided into user static preferences and user dynamic preferences.The former is estimated based on location category information and historical ratings.Meanwhile,the latter is evaluated based on geographical information and two-dimensional cloud model.The advantage of this method is that it not only considers the diversity of user preferences,but also can alleviate the data sparsity problem.In order to predict user preferences of new locations more precisely,the scheme integrates the similarity of user static preferences,user dynamic preferences and social ties into CF algorithm.Furthermore,the scheme is paraltelized on the Hadoop platform for significant improvement in efficiency.Experimental results on Yelp dataset demonstrate the performance gains of the scheme.     

Load-Balancing Based Cross-Layer Elastic Resource Allocation in Mobile Cloud

The paper proposes a hybrid mobile cloud computing system, in which mobile applications can use different resources or services in local cloud and remote public cloud such as computation, storage and bandwidth. The cross-layer load-balancing based mobile cloud resource allocation optimization is proposed. The proposed approach augments local cloud service pools with public cloud to increase the probability of meeting the service level agreements. Our problem is divided by public cloud service allocation and local cloud service allocation, which is achieved by public cloud supplier, local cloud agent and the mobile user. The system status information is used in the hybrid mobile cloud computing system such as the preferences of mobile applications, energy, server load in cloud datacenter to improve resource utilization and quality of experience of mobile user. Therefore, the system status of hybrid mobile cloud is monitored continuously. The mathematical model of the system and optimization problem is given. The system design of load-balancing based cross-layer mobile cloud resource allocation is also proposed. Through extensive experiments, this paper evaluates our algorithm and other approaches from the literature under different conditions. The results of the experiments show a performance improvement when compared to the approaches from the literature.

     

The Adaptive Ontology-Based Personalized Recommender System

Recommender systems provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in various areas such as e-commerce and e-learning. In this paper, we propose a hybrid recommendation strategy of content-based and knowledge-based methods that are flexible for any field to apply. By analyzing the past rating records of every user, the system learns the user’s preferences. After acquiring users’ preferences, the semantic search-and-discovery procedure takes place starting from a highly rated item. For every found item, the system evaluates the Interest Intensity indicating to what degree the user might like it. Recommender systems train a personalized estimating module using a genetic algorithm for each user, and the personalized estimating model helps improve the precision of the estimated scores. With the recommendation strategies and personalization strategies, users may have better recommendations that are closer to their preferences. In the latter part of this paper, a real-world case, a movie-recommender system adopting proposed recommendation strategies, is implemented.     

Agent-based meeting scheduling: a design and implementation

The Letter describes the design and implementation of a distributed meeting scheduling system in which each user has an intelligent agent in their computer desktop which is responsible for arranging meetings. Knowing the preferences and commitments of their user, the agents negotiate with one another to find the most acceptable meeting times     

IM(S)2: Interactive movie summarization system

The need of summarization methods and systems has become more and more crucial as the audio-visual material continues its critical growth. This paper presents a novel vision and a novel system for movies summarization. A video summary is an audio-visual document displaying the essential parts of an original document. However, the definition of the term “essential” is user-dependent. The advantage of this work, unlike the others, is the involvement of users in the summarization process. By means of IM(S)², people generate on the fly customized video summaries responding to their preferences. IM(S)² is made up of an offline part and an online part. In the offline, we segment the movies into shots and we compute features describing them. In the online part users inform about their preferences by selecting interesting shots. After that, the system will analyze the selected shots to bring out the user’s preferences. Finally the system will generate a summary from the whole movie which will provide more focus on the user’s preferences. To show the efficiency of IM(S)², it was tested on the database of the European project MUSCLE made up of five movies. We invited 10 users to evaluate the usability of our system by generating for every movie of the database a semi-supervised summary and to judge at the end its quality. Obtained results are encouraging and show the merits of our approach.