CROC: A New Evaluation Criterion for Recommender Systems

Evaluation of a recommender system algorithm is a challenging task due to the many possible scenarios in which such systems may be deployed. We have designed a new performance plot called the CROC curve with an associated statistic: the area under the curve. Our CROC curve supplements the widely used ROC curve in recommender system evaluation by discovering performance characteristics that standard ROC evaluation often ignores. Empirical studies on two domains and including several recommender system algorithms demonstrate that combining ROC and CROC curves in evaluation can lead to a more informed characterization of performance than using either curve alone.     

互联网推荐系统比较研究

全面地总结推荐系统的研究现状,旨在介绍网络推荐的算法思想、帮助读者了解这个研究领域.首先阐述了推荐系统研究的工业需求、主要研究机构和成果发表的期刊会议;在讨论了推荐问题的形式化和非形式化定义之后,对主流算法进行了分类和对比;最后总结了常用数据集和评测指标,领域的重难点问题和未来可能的研究热点.     

Recommender Systems Research: A Connection-Centric Survey

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.     

基于信任管理机制的推荐系统研究

针对用户兴趣迁移与用户对推荐行为无自主控制两个典型问题,提出了一种新颖的个性化协同过滤推荐策略.通过一种信任管理机制,用户自己管理信任关系.各用户拥有一个列表用来存放信任用户以及信任关系程度,通过用户自主行为可以控制系统的推荐.在推荐过程中,将信任度与相似度合并,得到复合权值,并以此进行推荐.同时,通过比较实际评价值与期望评价值,实时调整信任关系程度.实验结果表明,该策略提高了系统推荐的准确率,一定程度上增强了用户的信心.     

社会化推荐系统综述

推荐系统可以帮助网民从大量纷繁的信息中找到目标信息,能有效提高网民信息检索能力,然而推荐系统存在数据稀疏性、冷启动以及系统性能方面的问题。为解决这方面的问题,提出将社交关系应用于推荐系统,该方法是提高推荐准确性的一个重要途径,在多年的科研实践中取得了重要进展,因此该研究方向也日益成为众多学者关注的领域,有关这方面的研究也越来越活跃。通过对社会化推荐系统概念进行梳理,对社会化推荐系统与传统推荐系统进行比较,回顾总结了社会化推荐系统的研究现状,希望能从研究现状中找出新规律,寻求新的突破点,并对社会化推荐系统的发展趋势进行展望,以期对后来研究者有所帮助。     

教育网站个性化推荐系统中的一种用户兴趣模型的建立

用户兴趣模型用于描述用户的个人信息、专业背景、偏好倾向和历史行为等,通过这些信息,系统可以发现和预测用户的信息需求,从而对用户进行个性化的信息推荐服务.用户兴趣模型是影响推荐系统服务效率的重要因素,因此针对用户兴趣进行建模是个性化推荐系统实现中要重点考虑的问题之一.本文从教育网站用户对象特点出发,提出了将用户兴趣分为固定兴趣与临时兴趣相结合的动态模型.     

教育网站个性化推荐系统中的一种用户兴趣模型的建立

用户兴趣模型用于描述用户的个人信息、专业背景、偏好倾向和历史行为等，通过这些信息，系统可以发现和预测用户的信息需求，从而对用户进行个性化的信息推荐服务。用户兴趣模型是影响推荐系统服务效率的重要因素，因此针对用户兴趣进行建模是个性化推荐系统实现中要重点考虑的问题之一。本文从教育网站用户对象特点出发，提出了将用户兴趣分为固定兴趣与临时兴趣相结合的动态模型：     

电子商务推荐系统中的协同过滤推荐

电子商务推荐系统中协同过滤已成为目前应用最广泛、最成功的推荐方法。它利用相似用户购买行为也可能相似的特性进行推荐。介绍了与其他方法比较协同过滤方法的优点,然后说明了一些主要的协同过滤实现方法,指出了还需改进和完善的地方以及未来研究的方向。     

Enhancing Product Recommender Systems on Sparse Binary Data

Commercial recommender systems use various data mining techniques to make appropriate recommendations to users during online, real-time sessions. Published algorithms focus more on the discrete user ratings instead of binary results, which hampers their predictive capabilities when usage data is sparse. The system proposed in this paper, e-VZpro, is an association mining-based recommender tool designed to overcome these problems through a two-phase approach. In the first phase, batches of customer historical data are analyzed through association mining in order to determine the association rules for the second phase. During the second phase, a scoring algorithm is used to rank the recommendations online for the customer. The second phase differs from the traditional approach and an empirical comparison between the methods used in e-VZpro and other collaborative filtering methods including dependency networks, item-based, and association mining is provided in this paper. This comparison evaluates the algorithms used in each of the above methods using two internal customer datasets and a benchmark dataset. The results of this comparison clearly show that e-VZpro performs well compared to dependency networks and association mining. In general, item-based algorithms with cosine similarity measures have the best performance.     

一个基于VSM的个性化信息推荐系统

为了帮助用户从Internet上方便地获得信息，针对Proxy用户环境设计了一个信息推荐系统。先通过Proxy日志挖掘获得用户兴趣，再根据向量空间模型为用户生成兴趣特征，并据此对用户进行信息推荐。系统通过服务评估和反馈来保证信息推荐的质量。     

基于用户特征聚类联合情境特征的多维度应用推荐系统

随着移动设备的普及、同时大数据时代数据过载问题的日益严重,如何更准确地根据用户的兴趣及行为向用户推荐其可能感兴趣的应用软件成为亟待解决的问题.现有的推荐系统方法大多面临着推荐内容较为单一乏味等问题,且在推荐时没有将用户所处情境加以考虑,导致推荐效果欠佳.该文提出一种基于用户特征聚类联合情境特征的多维度应用推荐系统.经奇...     

基于AntStream用户聚类的协同过滤推荐系统

系统规模的逐步扩大和用户兴趣的发展变化给传统协同过滤推荐系统带来了实时性减弱和准确性降低的问题。基于K—Means用户聚类的协同过滤技术虽然能在一定程度上解决这两个问题,算法本身却带有局部最优的缺陷。在保证实时性的前提下,为克服K—Means算法的缺陷,提出使用AntClass蚁群算法对用户聚类。同时提出将用户评分看作数据流,利用金字搭时间框架预处理数据,从而体现用户兴趣随时间的变化。于是,将AntClass蚁群算法和利用金字塔时间框架预处理过的数据流相结合,最终形成文中的AntStream算法。实验表明,AntStream算法不仅改善了传统协同过滤推荐系统的实时性问题,而且更大程度提高了推荐质量。     

基于模糊兴趣模型的个性化推荐算法

论文在分析现有个性化推荐算法的基础之上,针对个性化的本质特点,结合模糊数学的知识,提出了一种个性化模糊兴趣模型,并建立一对应的推荐算法。实验表明,该模型有着简单,方便,快速推荐的特点。     

User Modeling for Adaptive News Access

We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.     

Identifying the effects of SVD and demographic data use on generalized collaborative filtering

The purpose of this paper is to examine how singular value decomposition (SVD) and demographic information can improve the performance of plain collaborative filtering (CF) algorithms. After a brief introduction to SVD, where the method is explained and some of its applications in recommender systems are detailed, we focus on the proposed technique. Our approach applies SVD in different stages of an algorithm, which can be described as CF enhanced by demographic data. The results of a rather long series of experiments, where the proposed algorithm is successfully blended with user- and item-based CF, show that the combined utilization of SVD with demographic data is promising, since it does not only tackle some of the recorded problems of recommender systems, but also assists in increasing the accuracy of systems employing it.     

基于用户相似性传递的跨平台交叉推荐算法

个性化推荐系统在电子商务领域中的广泛应用带来了巨大的经济效益和良好的用户体验。由于用户数据往往分布在多个不同的网站,单个网站的推荐系统受制于数据稀疏性的限制,难以获得准确的推荐效果。该文提出了一种基于传递相似性的交叉推荐系统算法,可以利用多个网站平台数据计算不同网站中的用户的相似度,从而很大程度上克服了推荐系统中的数据稀疏性以及冷启动问题。结果显示,该交叉推荐算法与传统的针对单个数据集的推荐算法相比,推荐的精确性有一至两倍的提高。     

The pragmatic quality of Resources‐ Events‐Agents diagrams: an experimental evaluation

The Resources‐Events‐Agents (REA) model is a semantic data model for the development of enterprise information systems. Although this model has been proposed as a benchmark for enterprise information modelling, only few studies have attempted to empirically validate the claimed benefits of REA modelling. Moreover, these studies focused on the evaluation of REA‐based system implementations rather than directly assessing the REA‐modelled conceptual schemas that these systems are based on. This paper presents a laboratory experiment that measured the user understanding of diagrammatic conceptual schemas developed using the REA model. The theoretical foundation for the hypotheses are cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding conceptual schemas. The results of the experiment indicate a more accurate understanding of the business processes and policies modelled when users recognize the REA model’s core pattern of enterprise information in the diagram. The implication for modelling practice is that the use of the REA model improves the requirements engineering process by facilitating the user validation of conceptual schemas produced by analysts, and thus helps ensuring the quality of the enterprise information system that is developed or implemented.     

基于隐私保护的联邦推荐算法综述

推荐系统通过集中式的存储与训练用户对物品的海量行为信息以及内容特征, 旨在为用户提供个性化的信息服务与决策支持. 然而, 海量数据背后存在大量的用户个人信息以及敏感数据, 因此如何在保证用户隐私与数据安全的前提下分析用户行为模式成为了近年来研究的热点. 联邦学习作为新兴的隐私保护范式, 能够协调多个参与方通过模型参数或者梯度等信息共同学习无损的全局共享模型, 同时保证所有的原始数据保存在用户的终端设备, 较之于传统的集中式存储与训练模式, 实现了从根源上保护用户隐私的目的, 因此得到了众多推荐系统领域研究学者们的广泛关注. 基于此, 对近年来基于联邦学习范式的隐私保护推荐算法进行全面综述、系统分类与深度分析. 具体的, 首先综述经典的推荐算法以及所面临的问题, 然后介绍基于隐私保护的推荐系统与目前存在的挑战, 随后从多个维度综述结合联邦学习技术的推荐算法, 最后对该方向做出系统性的总结并对未来研究方向与发展趋势进行展望.     

基于项目分类和用户群体兴趣的协同过滤算法

由于用户评分数据在极端稀疏的情况下会导致传统协同过滤算法的推荐质量下降,针对该问题,提出一种基于项目分类和用户群体兴趣的协同过滤算法。该算法根据项目类别信息对项目进行分类,相同分类的项目具有较高的相似性;利用评分数据计算各个项目分类上的用户相似性矩阵,并计算用户群体在各个分类上的兴趣,通过二者构造加权的用户相似性矩阵;利用用户加权相似性矩阵寻找用户的最近邻以获得最佳的推荐效果。实验结果表明,该算法能有效提高推荐质量。     

个性化服务技术综述

对个性化服务技术中用户描述文件的表达与更新、资源描述文件的表达、个性化推荐技术、个性化服务体系结构以及该领域的主要研究成果进行了综述.通过比较现有原型系统的实现方式,详细讨论了实现个性化服务的关键技术.此外,分析了3个具有代表性的个性化服务系统.最后对个性化服务技术进一步研究工作的方向进行了展望.