An adaptive personalized news dissemination system

With the explosive growth of the Word Wide Web, information overload became a crucial concern. In a data-rich information-poor environment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became a tedious task. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems are available for public use. In this work, we bridge theory to practice, by implementing a web-based news reader enhanced with a specifically designed machine learning framework for dynamic content personalization. This way, we get the chance to examine applicability and implementation issues and discuss the effectiveness of machine learning methods for the classification of real-world text streams. The main features of our system named PersoNews are: (a) the aggregation of many different news sources that offer an RSS version of their content, (b) incremental filtering, offering dynamic personalization of the content not only per user but also per each feed a user is subscribed to, and (c) the ability for every user to watch a more abstracted topic of interest by filtering through a taxonomy of topics. PersoNews is freely available for public use on the WWW ().

基于Hadoop平台的个性化新闻推荐系统的设计

为使互联网用户快捷地查找所需信息,个性化推荐系统的优势得到了体现和运用。该系统设计的目的是为广大网民在浏览新闻时提供一个个性化的新闻推荐系统,实现对新闻数据的协同过滤推荐处理。系统利用Hadoop的MapReduce模型实现并行快速地聚类海量新闻数据,大大提高了数据处理的速度,聚类使得新闻之间的相似度得以体现,再利用不同的协同过滤算法实现个性化的新闻推荐。     

Automatic and fast temporal segmentation for personalized news consuming

Automatic news program segmentation and classification becomes a hot topic, which reorganizes the news program according to the news’ topics, and provides the on-demand services to mobile consumers or Internet/home TV consumers. This paper presents a personalized news consuming system, including the system architecture, consumption steps and key techniques. Then, focused on the core technique, i.e., video temporal segmentation, the automatic video temporal segmentation method is proposed, evaluated and compared with existing ones. Experimental results show that the proposed scheme is computational efficient and gets higher correct detection rate. These properties make it a suitable choice for the personalized news consuming system.     

Modeling and broadening temporal user interest in personalized news recommendation

User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user’s fine-grained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user’s preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user’s profile. To activate users’ reading motivations, a successful recommender needs to introduce “somewhat novel” articles to users.In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that a user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen as the recommended candidates based on the short-term user profile. We further propose to select news items from the user–item affinity graph using absorbing random walk model to increase the diversity of the recommended news list. Extensive empirical experiments on a collection of news data obtained from various popular news websites demonstrate the effectiveness of our method.     

个性化学习系统的聚类技术

基于日志的Web使用挖掘,利用用户访问页面的相关性提出用户兴趣度,并应用于远程教育中数据准备和页面的推荐过程．讨论教学过程中按需学习和因才施教的可行性,介绍聚类算法在预测推荐页面中的设计与应用．实验运行结果表明,该算法是可行和有效的．     

个性化推荐系统综述

信息超载是目前网络用户面临的一个严重问题,个性化推荐系统是解决该问题的一个有力工具,并受到了众多的关注和研究。给出推荐系统的定义,同时阐述了推荐系统的几项关键技术,包括用户建模、推荐对象的建模和推荐算法。后来总结了推荐系统的体系结构和性能评价指标,并尝试给出了推荐系统未来研究的重点、难点和热点问题。     

基于ASP模式的个性化应用系统模型

对传统的企业信息化建设方法与ASP建设模式进行了比较分析，讨论了个性化应用服务的基本概念、需求以及Web相关信息的形式化描述方法，然后提出了一个基于ASP模式的个性化应用系统模型。该模型主要思想是将企业的一些具体业务应用系统从企业中独立出来，通过Web应用平台为用户提供各种个性化的信息服务和应用服务。与传统的建设方法相比，提出的基于ASP模式的个性化应用系统模型设计方法具有投资小、见效快、信息服务质量高等特点。     

个性化食谱智能优化系统

为提高食谱设计质量与效率,提出一种基于交互式进化计算的食谱智能优化方法.根据用户评价值和食谱菜品优化模型确定食谱样本隐式指标与显式指标;基于NSGA-Ⅱ算法获得食谱样本Pareto优化解.为使Pareto优化解更好兼顾用户偏好与营养均衡,引入指标均衡度指导进化.当指标均衡度较低时,采用遗传算法模块对显式指标单独进化,提...     

IoT-based personalized NIE content recommendation system

Multimedia Tools and Applications - Recently, the Internet of Things (IoT) has become a popular topic and a dominant trend in various fields, such as healthcare, agriculture, manufacturing, and...     

基于模糊描述逻辑的个性化推荐系统建模*

为了解决现有个性化推荐系统中缺乏对模糊语义信息处理的能力,本文建立模糊语义推荐系统模型,使用模糊描述逻辑实现了该模型,并给出了推荐算法。在实现模型的过程中引入了两条规则实现了概念层次关系在的兴趣程度和关联程度上的传递。最后通过实例证明,通过将用户的兴趣和待选资源的相关概念在语义层面进行适当的扩展,模糊语义推荐系统模型能更准确描述用户的兴趣并产生更多符合用户兴趣的推荐项目。     

基于多重因素的个性化学习推荐系统

为解决现有学习推荐算法中存在的忽略对学生知识点掌握情况的分析、不能将知识掌握程度概率化等问题,提出一种基于多重因素的学习推荐方法。该方法综合考虑知识点的综合权重、错误率和失分率多个因素构建知识点掌握概率模型,并应用所提出的策略实现一个在线的个性化学习推荐系统。系统评估上对200名高中生进行了一项调查,本系统推荐top-8知识点的准确率达到91.2％,◢F◣▼1▽达到78.4％。系统调查的结果显示了提出策略的有效性和可靠性。     

A robust NFC-based personalized IPTV service system

Internet Protocol Television (IPTV) is becoming a platform that changes the way we obtain information and entertainment, and offers interactive features and personalized services. Although IPTV service providers can perform TV viewer identification and authentication through a unique hardware identifier in the form of a set-top box (STB), it is based on STB-level identification which leads to the situation where all members of a subscriber family get the same level of access to services. This indicates that existing identification schemes are inconsistent with IPTV’s main intent, namely, providing personalized services. Smartphones with NFC (Near Field Communication) capabilities have grown to become very popular over the years. In this study, we present a novel personalized IPTV service system in which NFC-based identification with HCE (Host Card Emulation) is adopted. The experiments and analyses show that the proposed system can meet the system requirements and provide great usability, deployability and service scalability for personalized IPTV services.     

基于柔性的个性化服务系统的开发过程

实现互联网信息的个性化服务,是Web信息处理中的一个重要研究课题。为了有效解决个性化服务系统动态适应用户需求变化的问题,文章结合柔性的思想,提出基于柔性的个性化信息服务的概念,并给出了实现基于柔性的个性化信息服务系统的具体思路和解决方案。基于柔性的个性化信息服务将成为个性化服务的新模式。     

资源自适应的实时新闻推荐系统

为解决新闻推荐系统性能差、效率低等问题,更好地满足商业应用的需要,设计了基于内容的资源自适应实时新闻推荐系统EagleNews.该系统自动监控系统负载情况,通过自动调整被推荐新闻集合的时间窗口,控制新闻数量,调整文档向量和用户模型向量的维度,优化相似度计算速度,提高系统性能,同时兼顾了推荐效果.最后,在原型系统上对提出的方法进行了评测,获得了系统运行的最佳参数,表明该系统不仅具有良好的性能,同时能够提供较好的推荐效果.     

Private personalized social recommendations in an IPTV system

In our connected world, recommender systems have become widely known for their ability to provide expert and personalize referrals to end-users in different domains. The rapid growth of social networks and new kinds of systems so called “social recommender systems” are rising, where recommender systems can be utilized to find a suitable content according to end-users' personal preferences. However, preserving end-users' privacy in social recommender systems is a very challenging problem that might prevent end-users from releasing their own data, which detains the accuracy of extracted referrals. In order to gain accurate referrals, social recommender systems should have the ability to preserve the privacy of end-users registered in this system. In this paper, we present a middleware that runs on end-users' Set-top boxes to conceal their profile data when released for generating referrals, such that computation of recommendation proceeds over the concealed data. The proposed middleware is equipped with two concealment protocols to give users a complete control on the privacy level of their profiles. We present an IPTV network scenario and perform a number of different experiments to test the efficiency and accuracy of our protocols. As supported by the experiments, our protocols maintain the recommendations accuracy with acceptable privacy level.     

User-adapted travel planning system for personalized schedule recommendation

Recently, the Internet has made a lot of services and products appear online provided by many tourism sectors. By this way, many information such as timetables, routes, accommodations, and restaurants are easily available to help travelers plan their travels. However, how to plan the most appropriate travel schedule under simultaneously considering several factors such as tourist attractions visiting, local hotels selecting, and travel budget calculation is a challenge. This gives rise to our interest in exploring the recommendation systems with relation to schedule recommendation. Additionally, the personalized concept is not implemented completely in most of travel recommendation systems. One notable problem is that they simply recommended the most popular travel routes or projects, and cannot plan the travel schedule. Moreover, the existing travel planning systems have limits in their capabilities to adapt to the changes based on users’ requirements and planning results. To tackle these problems, we develop a personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service that approximates automation. A novel travel schedule planning algorithm is embedded to plan travel schedules based on users’ need. Through the user-adapted interface and adjustable results design, users can replace any unsatisfied travel unit to specific one. The feedback mechanism provides a better accuracy rate for next travel schedule to new users. An experiment was conducted to examine the satisfaction and use intention of the system. The results showed that participants who used the system with schedule planning have statistical significant on user satisfaction and use intention. We also analyzed the validity of applying the proposed algorithm to a user preference travel schedule through a number of practical system tests. In addition, comparing with other travel recommendation systems, our system had better performance on the schedule adjustment, personalization, and feedback giving.     

基于Web的个性化学习系统的设计

为改善基于Web学习系统存在的不足,提出了一个基于数据挖掘技术的个性化学习系统模型,并详细描述了应用决策树及BP神经网络算法对个性化导航模块设计的方法.应用决策树方法,根据学生初始注册信息,为学生的学习能力进行分类;应用BP神经网络算法,对经过预处理的有用的教学数据进行挖掘,以得出学生对知识点的掌握情况;在分析对比学生的学习状态与课程要求的基础上为学生提供下一步学习的导航信息.基于该模型实现的个性化学习系统真正体现了因材施教的教育理念.     

个性化搜索引擎系统机制的研究

随着网络信息资源的迅速增加,个性化信息服务越来越成为信息检索领域中研究的热点,针对传统搜索引擎系统的缺点,提出了一种新型个性化搜索引擎系统的体系结构,并在此基础上给出了系统中个性化机制的相关算法,同时使用基于关键词的搜索,利用Web挖掘技术,在实现为不同用户提供不同检索结果的同时提高了个性化查询的精确度和速度,保证了全查率.     

A Personalized Information Dissemination System Based on How—Net

The information dissemination model is becoming increasingly important in wide-area information systems,In this model,a user subscribes to an information dissemination service by submitting profiles that describe his interests.There have been several simple kinds of information dissemination services on the Internet such as mailing list,but the problem is that it provides a crude granularity of interest matching.A user whose information need does not exactly match certain lists will either receive too many irrelevant or too few relevant messages.This paper presents a personalized information dissemination model based on HowNet,which uses a Concept Network-Views(CN-V) model to support information filtering,user‘s interests modeling and information recommendation.A Concept Network is constructed upon the user‘s profiles and the content of documents,which describes concepts and their relations in the content and assigns different weights to these concepts.Usually the Concept Network is not well arranged,from which it is hard to find some useful realtions.so several views from are extracted it to represent the important relations explicitly.