A survey of autoencoder-based recommender systems

In the past decade,recommender systems have been widely used to provide users with personalized products and services.However,most traditional recommender systems are still facing a challenge in dealing with the huge volume,complexity,and dynamics of information.To tackle this challenge,many studies have been conducted to improve recommender system by integrating deep learning techniques.As an unsupervised deep learning method,autoencoder has been widely used for its excellent performance in data dimensionality reduction,feature extraction,and data reconstruction.Meanwhile,recent researches have shown the high efficiency of autoencoder in information retrieval and recommendation tasks.Applying autoencoder on recommender systems would improve the quality of recommendations due to its better understanding of users,demands and characteristics of items.This paper reviews the recent researches on autoencoder-based recommender systems.The differences between autoencoder-based recommender systems and traditional recommender systems are presented in this paper.At last,some potential research directions of autoencoder-based recommender systems are discussed.     

基于语义聚类的协作推荐攻击检测模型

协作过滤推荐模型目前已被广泛应用于电子商务等环境。由于其对用户偏好数据敏感,因此攻击者可以通过注入伪造的用户偏好数据来影响推荐系统的预测。提出了一个基于语义聚类的协作过滤攻击检测模型,从分析项目的语义入手,针对攻击数据中的随机性,通过分析用户兴趣的组合来评判用户偏好数据的真实与否。大量的实验证明,该模型能有效地检测协作过滤推荐中的注入攻击,从而大大提高了推荐系统的鲁棒性和可靠性。     

音乐推荐系统及相关技术研究

随着计算机网络和多媒体技术的迅速普及,推荐系统特别是音乐推荐系统成为了电子商务领域的一大研究热点.对现有的音乐推荐系统及其相关技术作了全面深入的调查和分析,在介绍个人及群体音乐推荐系统的基础上,讨论了音乐推荐系统的发展方向并提出了相应的研究重点.该课题的研究有助于提高音乐推荐质量,增强用户满意度,提高音乐推荐系统的实际可用性.相关研究成果也将对其他推荐系统以及多媒体应用系统的研究起到重要的借鉴作用.     

电子商务推荐系统中推荐策略的自适应性

针对电子商务推荐系统中各种推荐技术的不足,提出推荐策略的自适应方法。用二元组《用户知识,推荐商品》代表推荐环境的根本特征．采用ART神经网络进行自学习,获取推荐环境的不同聚类。每个聚类代表了某种推荐环境,对推荐结果的反馈情况进行统计分析．确定每个聚类的最佳推荐技术。向用户推荐商品时,根据用户所在聚类采用具有最佳推荐质量的推荐技术向用户作出推荐。整个系统的工作过程不需要人工干预,具有自适应性。     

语义Web本体及本体库系统设计技术

当前的Web页面使用格式化表示语言描述信息,由于其以自然语言形式描述信息,因而不利于机器理解,为此而出现了语义Web这一研究领域,旨在实现Web的机器理解。文中介绍了语义Web的结构,然后对语义Web模型中的本体模型作了深入的讨论,包括本体的概念、构建本体的原因以及一个本体库系统所应具有的功能,并给出了有关功能结构图,最后讨论了未来的研究方向。     

Power coefficient as a similarity measure for memory-based collaborative recommender systems

E-commerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. However, choosing appropriate similarity measure is a key to the recommender system success. Based on this measure, a set of neighbors for the current active user is formed which in turn will be used later to recommend unseen items to this active user. Pearson correlation coefficient, the most popular similarity measure for memory-based collaborative recommender system (CRS), measures how much two users are correlated. However, statistic’s literature introduced many other coefficients for matching two sets (vectors) that may perform better than Pearson correlation coefficient. This paper explores Jaccard and Dice coefficients for matching users of CRS. A more general coefficient called a Power coefficient is proposed in this paper which represents a family of coefficients. Specifically, Power coefficient gives many degrees for emphasizing on the positive matches between users. However, CRS users have positive and negative matches and therefore these coefficients have to be modified to take negative matches into consideration. Consequently, they become more suitable for CRS research. Many experiments are carried out for all the proposed variants and are compared with the traditional approaches. The experimental results show that the proposed variants outperform Pearson correlation coefficient and cosine similarity measure as they are the most common approaches for memory-based CRS.     

引入评分偏置的二项矩阵分解推荐算法

针对推荐系统中的评分预测问题,在矩阵分解的基础上实现了一种修正的二项矩阵分解算法。假设用户对物品的评分基于二项分布,由于用户的评分习惯存在差异,物品的受欢迎程度也存在差异,导致用户—物品评分矩阵存在偏置量。通过引入偏置量对矩阵分解和评分预测进行修正,采用最大后验估计建模,并通过随机梯度下降算法优化模型。实验结果表明,在MovieLens 100K数据集上,引入评分偏置的二项矩阵分解算法在推荐精度、离线计算时间等方面均优于传统的二项矩阵分解算法。     

Onyx: A new Canvas-based tool for visualizing biomedical and health ontologies

Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation.     

A novel framework to alleviate the sparsity problem in context-aware recommender systems

Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.     

A novel classification-based shilling attack detection approach for multi-criteria recommender systems

Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi-criteria rating-based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power-item attack model, and introduce its four distinct variants adapted for multi-criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model-based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that the proposed method successfully detects attack profiles from genuine users even with a small selected size and attack size. The empirical outcomes also demonstrate that item popularity and user characteristics based on their rating profiles are highly beneficial features in capturing shilling attack profiles.     

A research on social network-based personalized semantic retrieval model

Personalized semantic retrieval extends the query process and optimizes query results by mapping user preference of information to ontology. It can fetch different results according to the same queries from different users. This paper proposes a personalized semantic retrieval model based on social network. It implements the organization, presentation, acquisition and maintenance of user preference data. Finally, it uses these personalization data in the process of information retrieval.     

语义Web与本体研究综述

语义Web是一个新兴的研究方向,Ontology在语义Web中的本体应用研究还在初级阶段。介绍了Ontology的定义和描述语言,建设方法和工具,以及主要研究机构,并介绍了目前语义Web中Ontology的研究和成果。     

一种基于本体的Web服务发现框架

Web服务的大量涌现对服务发现提出了挑战.目前基于关键字和基于框架的服务发现机制,将查询结果通过一定的排序法则呈现在用户面前.但是这些发现方法查准率极其低下,已经不能很好地满足用户需要.在研究本体与Web服务的基础上,提出了一种基于本体的Web服务发现框架,该框架首先对用户的请求合约进行语义预处理,然后根据抽取的语义在服务库中发现适合Web服务,从而提高查准率.     

A survey of recommender systems for energy efficiency in buildings: Principles,challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems’ performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors’ knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.     

一种基于本体的Web服务发现框架

Web服务的大量涌现对服务发现提出了挑战。目前基于关键字和基于框架的服务发现机制,将查询结果通过一定的排序法则呈现在用户面前。但是这些发现方法查准率极其低下,已经不能很好地满足用户需要。在研究本体与Web服务的基础上．提出了一种基于本体的Web服务发现框架．该框架首先对用户的请求合约进行语义预处理．然后根据抽取的语义在服务库中发现适合Web服务,从而提高查准率。     

一种基于语义Web的Web服务发现方法

Web服务的大量涌现对服务发现提出了挑战。然而,传统的服务发现技术是建立在语法描述的基础上,主要采用的服务发现机制是WSDL和UDDI规范相结合的解决方案,通过关键词匹配来实现的。但是这些发现方法查准率极其低下,已经不能很好地满足用户需要。在研究本体与语义Web服务的基础上,提出了一种基于本体的Web服务发现框架,该框架首先对用户的请求合约进行语义预处理,然后根据抽取的语义在服务库中发现适合的Web服务,从而提高查准率。     

Learning with personalized recommender systems: A psychological view

This paper explores the potentials of recommender systems for learning from a psychological point of view. It is argued that main features of recommender systems (collective responsibility, collective intelligence, user control, guidance, personalization) fit very well to principles in the learning sciences. However, recommender systems should not be transferred from commercial to educational contexts on a one-to-one basis, but rather need adaptations in order to facilitate learning. Potential adaptations are discussed both with regard to learners as recipients of information and learners as producers of data. Moreover, it is distinguished between system-centered adaptations that enable proper functioning in educational contexts, and social adaptations that address typical information processing biases. Implications for the design of educational recommender systems and for research on educational recommender systems are discussed.     

基于项的协同过滤在推荐系统中的应用研究

分析基于项的协同过滤在推荐系统中应用及所存在的问题,提出了一个基于项的协同过滤改进算法,并给出了改进算法在标准数据集上的实验结果,对改进算法与原算法进行了相关性能的比较分析,证明了改进算法的有效性.最后,对研究进行了总结,指出存在的不足,提出了进一步研究的方向.     

语义元数据的描述及生成技术研究

语义元数据是有关Web内容语义信息的数据描述,它的有效表示及生成是构建语义Web的关键性技术。本文在讨论各种语义元数据的表示方法后,研究语义元数据的生成技术,在分析现有技术的特点和不足后,评述语义元数据生成技术的发展趋势。