P2P网络环境下的一种高效虚拟协同服务模型

在现实网络中,资源主要集中在少数的重要节点中,大量节点是服务请求者.由于P2P网络是建立在Internet之上的应用层虚拟网络,加上网络中搭便车现象日益严重,在广域环境下,不可避免地存在拥塞、单点失效、效率和服务质量不高的问题.针对这些问题,通过在系统中动态地构造由协同服务盟员组成的服务池来解决.提出了一种有盟主的虚拟协同服务组织模型,旨在现实环境下针对盟主的目标需求,解决盟主如何动态组织自主的协同伙伴和构建虚拟组织协同进行求解的问题.构造了基于D-S证据推理的服务盟员选择策略,运用节点交易历史信息和推荐证书的方法来表征备选服务盟员的全局信任特征属性,给出了构建虚拟协同服务池的数学模型、约束条件和构造规则,并进行了较为详细的分析.仿真实验表明,动态构造服务池的负载平衡策略能较好地解决P2P网络中存在拥塞、单点失效、效率和服务质量不高的问题,极大地改善了P2P网络的服务质量.     

基于协作过滤的个性化服务技术研究

随着网络的普及和发展以及网络信息量的日益增加,为广大用户提供个性化服务显得尤为必要.在对个性化服务技术相关知识进行概述的基础上介绍了协作过滤信息推荐技术的基本原理、分类、所面临的困难等,并对国内外研究现状等进行了综述.最后时基于协作过滤的个性化服务技术进一步的研究工作进行了展望.     

基于用户聚类的播客节目推荐

许多播客推荐机制一般根据整体点击次数来向用户推荐节目,但是一些点击次数很高的节目未必就是某类用户所喜欢的,因而推荐的节目对用户的喜好针对性不是很强.为了提高推荐节目对用户喜好特点的针对性,提出基于用户聚类的节目推荐.对用户在播客平台上的采集数据进行聚类分析后,把用户归为某一类型,并把该类型的所有节目根据点击次数多少存放入相应的推荐表.在用户下次登录时,根据其所属用户类型从推荐表中选出其最可能观看的尚未浏览的节目.试验结果证明,该播客推荐系统能很好地根据用户的喜好特点来进行针对性节目推荐.     

个性化学习资源推荐系统研究

通过分析远程网络学习系统中学习者对学习资源的访问历史,以及与学习者有类似访问兴趣的同组学习者的学习偏好,为学习者提供个性化的资源推荐服务,能够有效提高各种学习资源的利用效率,从而提高教学质量.     

协同过滤算法在推荐系统中的应用

介绍了协同过滤算法,并对算法进行了改进,解决了用户稀疏的情况下传统算法的不足,同时通过引入评分阈值,显著提高了个性化协同过滤算法的推荐精度。     

一种基于Web使用挖掘的个性化信息推荐系统实现方法

为了提高基于Web使用挖掘的个性化信息推荐的有效性和效率,提出了将顺序模式和KP混合聚类遗传算法相结合的推荐策略,在Web日志预处理的基础上,设计出了一种基于Web使用挖掘的个性化信息推荐系统RSPIBOWUM的框架结构,并给出了基于这一框架结构的实现流程。结果表明,本推荐策略可以进行有效的个性化信息推荐,提高个性化信息推荐的效率。     

电子商务系统的数据挖掘与智能推荐预测的研究

本文对数据挖掘的概念做了简要的描述,并对决策树、关联规则和聚类三种数据挖掘算法做了分析比较,认为决策树算法虽然对于每一个项有更详细的模式且支持连续的输入,但不能扩展为大的目录;关联规则算法虽然快速、可伸缩,但是对算法的参数非常敏感;聚类算法虽然按相似性对数据进行分组,但是要设置复杂的参数和变量。     

基于电子商务应用的协同过滤技术改进综述

电子商务个性化推荐具有重要的意义。协同过滤是电子商务个性化的重要实现技术之一。本文针对UBCF和IBCF方法在应用实践上的问题,总结了目前文献对于协同过滤技术的改进思路,归纳了对于CF推荐技术的各种新型算法技术文献,总结了目前对于CF技术不同的改进思路。     

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing. In this paper advanced model of the Logarithmic Directionality Texture Pattern (LDTP) method with a Metaheuristic Pattern Searching (MPS) system was used to estimate the similarity between the query data in the entire database. The overall work was implemented for the application of the data recommendation process. These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period. Also, with the help of a neural network, the relevancies of feature attributes in the database are predicted, and the matching index was sorted to provide the recommended data for given query data. This was achieved by using the Distributional Recurrent Neural Network (DRNN). This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set. The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset. These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric. The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%, 70%, and 80%. The parameters that are considered for performance analysis are Precision, Recall, F1-score and the accuracy of data retrieval, the query recommendation output, and comparison with other state-of-art methods.     

Health-guided recipe recommendation over knowledge graphs

While the availability of large-scale online recipe collections presents opportunities for health consumers to access a wide variety of recipes, it can be challenging for them to discover relevant recipes. Whereas most recommender systems are designed to offer selections consistent with users’ past behavior, it remains an open problem to offer selections that can help users’ transition from one type of behavior to another, intentionally. In this paper, we introduce health-guided recipe recommendation as a way to incrementally shift users towards healthier recipe options while respecting the preferences reflected in their past choices. Introducing a knowledge graph (KG) into recommender systems as side information has attracted great interest, but its use in recipe recommendation has not been studied. To fill this gap, we consider the task of recipe recommendation over knowledge graphs. In particular, we jointly learn recipe representations via graph neural networks over two graphs extracted from a large-scale Food KG, which capture different semantic relationships, namely, user preferences and recipe healthiness, respectively. To integrate the nutritional aspects into recipe representations and the recommendation task, instead of simple fusion, we utilize a knowledge transfer scheme to enable the transfer of useful semantic information across the preferences and healthiness aspects. Experimental results on two large real-world recipe datasets showcase our model’s ability to recommend tasty as well as healthy recipes to users.