Review based emotion profiles for cross domain recommendation

Several e-commerce sites are reaping the benefits of Cross-Domain Recommendation (CDR) systems to cross-sell products, guide new users and increase revenues. Current research works augment user-item ratings with a variety of auxiliary information such as location, personality, geo-tags and multimedia content that link multiple domains to provide effective CDR. In this paper, we propose a fresh perspective for generating recommendations across different domains by tapping the emotions that are encapsulated within user generated textual content such as reviews, blogs and comments. Such emotions serve as strong socio-psychological links between various entertainments domains and have the potential to obviate the cold start problems. Our CDR scheme uses an enriched emotion lexicon to analyze the emotions in online content expressed by users in the source and target domains and generates emotion-profiles of items and users in both domains. Subsequently, it applies collaborative filtering to match these profiles in order to recommend items in the target domain. We illustrate the working of our emotion-based CDR scheme using the movie and book domains as a case study. Experimental results on Movielens and Bookcrossing datasets yield 28.9% F1-measure which is a marked improvement of 71.1% as compared with a recently reported topic modeling approach to CDR for entertainment domains.     

The media-oriented cross domain recommendation method

Multimedia Tools and Applications - With the rapid development of modern high-tech, such as big data and artificial intelligence, the demand for cross-media services is also greatly improved. Since...     

Symbolic data analysis tools for recommendation systems

Recommender systems have become an important tool to cope with the information overload problem by acquiring data about user behavior. After tracing the user’s behavior, through actions or rates, computational recommender systems use information- filtering techniques to recommend items. In order to recommend new items, one of the three major approaches is generally adopted: content-based filtering, collaborative filtering, or hybrid filtering. This paper presents three information-filtering methods, each of them based on one of these approaches. In our methods, the user profile is built up through symbolic data structures and the user and item correlations are computed through dissimilarity functions adapted from the symbolic data analysis (SDA) domain. The use of SDA tools has improved the performance of recommender systems, particularly concerning the find good items task measured by the half-life utility metric, when there is not much information about the user.     

基于票据的跨域单点登录

针对单点登录中的跨域身份认证问题,提出了一种基于票据的解决方案,以地址重定向的方式传递加密用户登录信息,异域应用系统获取用户信息并提供数据操作服务。使用随机数字生成票据,并作为生成传统加密算法会话密钥的参数,采用现代加密算法实现异域系统之间的互信并安全传递票据,异域应用系统根据票据产生会话密钥,加密并传输用户登录信息,每次会话产生新的密钥。通过对票据产生和传输以及密钥的安全性分析,可以实现跨域单点登录的功能并保证身份认证安全可信。     

基于可变Cookie的跨域单点登录

针对单点登录中的跨域身份认证问题,提出了一种基于可变Cookie的方案解决跨域单点登录,使用随机数字生成票据,并作为传统加密算法的会话密钥对客户端的Cookie进行加密,采用现代加密算法在异域系统之间安全传递票据,每次认证产生新的票据并更新异域应用系统的Cookie。通过对票据产生和传输以及Cookie加密和常见攻击的安全性分析,可以实现跨域单点登录的功能并保证身份认证安全可信。     

知识表示学习研究进展

新型深度学习推荐模型已广泛应用至现代推荐系统,其独有的特征——包含万亿嵌入参数的嵌入层,带来的大量不规则稀疏访问已成为模型预估的性能瓶颈. 然而,现有的推荐模型预估系统依赖CPU对内存、外存等存储资源上的嵌入参数进行访问,存在着CPU-GPU通信开销大和额外的内存拷贝2个问题,这增加了嵌入层的访存延迟,进而损害模型预估的性能. 提出了一种基于GPU直访存储架构的推荐模型预估系统GDRec.GDRec的核心思想是在嵌入参数的访问路径上移除CPU参与,由GPU通过零拷贝的方式高效直访内外存资源. 对于内存直访,GDRec利用统一计算设备架构（compute unified device architecture,CUDA）提供的统一虚拟地址特性,实现GPU 核心函数（kernel）对主机内存的细粒度访问,并引入访问合并与访问对齐2个机制充分优化访存性能;对于外存直访,GDRec实现了一个轻量的固态硬盘（solid state disk,SSD）驱动程序,允许GPU从SSD中直接读取数据至显存,避免内存上的额外拷贝,GDRec还利用GPU的并行性缩短提交I/O请求的时间. 在3个点击率预估数据集上的实验表明,GDRec在性能上优于高度优化后的基于CPU访存架构的系统NVIDIA HugeCTR,可以提升多达1.9倍的吞吐量.

     

一种简单跨域单点登录系统的实现

分布式体系架构下多站点协作网络的应用需要统一身份认证和资源访问控制机制,单点登录系统是完成这项功能的必备模块。采用一种应用于Web环境下轻量级的单点登录解决方案,它是一种基于HTTP重定向和票据,并以跨域Cookie的共享为核心的集中式认证系统。本方案在分布式数据资源共享网络建设中实现了多个站点的跨域全局登录、用户认证和用户授权等功能。通过建立规范的登录控制模块,简单地修改配置文件,就可方便地将分散网络节点加入认证体系,完成网络节点单点登录和资源访问控制问题。     

Fractional Fourier domain equalization for single carrier broadband wireless systems

In this paper,a novel single carrier equalization approach in the fractional Fourier domain(FRFD) is proposed.It can remove all the inter-symbol interference(ISI) and avoid the considerable noise enhancement of the frequency domain-zero forcing(FD-ZF) equalizer.As the fractional Fourier transform makes a chirp spread,the impulse response of the deep fading channel may be flattened in some orders of the FRFD while it would be greatly attenuated in the FD.By searching for an optimal order under certain criterion,we take advantages of the ZF algorithm to mitigate the effects of the ISI completely.This approach can overcome the contradiction between the ISI mitigation and the noise enhancement of the FD-ZF equalizer.Theoretical analysis and simulation results show that the proposed FRFD-ZF equalizer can achieve a significant performance and have the same computation cost O(N log N) as the conventional FD linear equalizer,especially in the frequencyselective deep-fading channels.     

Adapting domain ontology for personalized knowledge search and recommendation

This paper presents research on the development of a domain ontology adaptation system for personalized knowledge search and recommendation that adapts a suitable domain ontology according to the previous browsing and reading behavior of users (i.e., usage history log). An adaptive domain ontology can satisfy the future requirements of users and promote use value. In developing the system, a domain ontology adaptation model is first designed. Based on the designed adaptation model, a methodology for domain ontology adaptation is developed. Subsequently, a domain ontology adaptation system is implemented with an illustrative example of securities trading. Finally, a system evaluation for user satisfaction and a methodology evaluation are conducted to demonstrate that the developed methodology and system worked efficiently.     

Ubiquitous recommendation systems

In many popular visions of ubiquitous computing, the environment proactively responds to individuals who inhabit the space. For example, a display magically presents a personalized advertisement, the most relevant video feed, or the desired page from a secret government document. Such capability requires more than an abundance of networked displays, devices, and sensors; it relies implicitly on recommendation systems that either directly serve the end user or provide critical services to some other application. As recommendation systems evolve to exploit new advances in ubiquitous computing technology, researchers and practitioners from technical and social science disciplines must collaborate to address the challenges to their effective implementation. Although it may be impossible to perfectly anticipate each individual's needs at any place or time, ubiquitous computing will enable such systems to help people cope with an expanding array of choices.     

DataGenCARS: A generator of synthetic data for the evaluation of context-aware recommendation systems

Context-Aware Recommender Systems (CARS) have started to attract significant research attention in the last years, due to the interest of considering the context of the user in order to offer him/her more appropriate recommendations. However, the evaluation of CARS is a challenge, due to the scarce availability of appropriate datasets that incorporate context information related to the ratings provided by the users.In this paper, we present DataGenCARS, a complete Java-based synthetic dataset generator that can be used to obtain the required datasets for any type of scenario desired, allowing a high flexibility in the obtention of appropriate data that can be used to evaluate CARS. The generator presents features such as: a flexible definition of user schemas, user profiles, types of items, and types of contexts; a realistic generation of ratings and attributes of items; the possibility to mix real and synthetic datasets; functionalities to analyze existing datasets as a basis for synthetic data generation; and support for the automatic mapping between item schemas and Java classes. Moreover, an experimental evaluation illustrates the interest and the benefits provided by DataGenCARS.     

基于位置和开放链接数据的旅游推荐系统综述

利用开放链接数据解决基于位置的推荐系统中存在的信息过载问题是目前的研究热点，并在旅游领域展现出了巨大的潜力。首先给出推荐系统的基本概况；然后对旅游开放链接数据进行了概况；从文献分类、应用分类和研究成果对基于位置和开放链接数据的旅游推荐系统从2014—2018年的相关文献进行了详细考察，并从基于位置的单点推荐、旅游路线推荐、GPS轨迹推荐、基于媒介的地理标签推荐、基于本体的位置推荐和基于位置的朋友推荐等6类典型的应用进行分类考察，最后对全文并为该领域指明了研究方向。     

Decision feedback data detection in frequency domain for single carrier cyclic prefix assisted CDMA systems: A sequential quadratic programming approach

This paper proposes a frequency domain decision feedback data receiver for the uplink transmission of broadband single carrier cyclic prefix-assisted CDMA systems. The optimum data detection problem based on the maximal-likelihood criteria for this system is addressed as a combinatorial optimization problem. A sequential quadratic programming approach is proposed to solve this problem. The parallel gradient projection method and projected successive over relaxation algorithm are proposed to solve the sequential quadratic programming problem, which corresponds to low-complexity nonlinear parallel and successive interference cancellation schemes in frequency domain. The convergence properties and the complexities of these methods are analyzed and compared with conventional methods. The simulation results show that, with a few iterations, the proposed scheme gives near single user performance even for fully loaded systems.     

An empirical study on the effect of data sparsity and data overlap on cross domain collaborative filtering performance

In the present day, the oversaturation of data has complicated the process of finding information from a data source. Recommender systems aim to alleviate this problem in various domains by actively suggesting selective information to potential users based on their personal preferences. Amongst these approaches, collaborative filtering based recommenders (CF recommenders), which make use of users’ implicit and explicit ratings for items, are widely regarded as the most successful type of recommender system. However, CF recommenders are sensitive to issues caused by data sparsity, where users rate very few items, or items receive very few ratings from users, meaning there is not enough data to give a recommendation. The majority of studies have attempted to solve these issues by focusing on developing new algorithms within a single domain. Recently, cross-domain recommenders that use multiple domain datasets have attracted increasing attention amongst the research community. Cross-domain recommenders assume that users who express their preferences in one domain (called the target domain) will also express their preferences in another domain (called the source domain), and that these additional preferences will improve precision and recall of recommendations to the user. The purpose of this study is to investigate the effects of various data sparsity and data overlap issues on the performance of cross-domain CF recommenders, using various aggregation functions. In this study, several different cross-domain recommenders were created by collecting three datasets from three separate domains of a large Korean fashion company and combining them with different algorithms and different aggregation approaches. The cross-recommenders that used high performance, high overlap domains showed significant improvement of precision and recall of recommendation when the recommendation scores of individual domains were combined using the summation aggregation function. However, the cross-recommenders that used low performance, low overlap domains showed little or no performance improvement in all areas. This result implies that the use of cross-domain recommenders do not guarantee performance improvement, rather that it is necessary to consider relevant factors carefully to achieve performance improvement when using cross-domain recommenders.     

Content-based filtering for recommendation systems using multiattribute networks

Content-based filtering (CBF), one of the most successful recommendation techniques, is based on correlations between contents. CBF uses item information, represented as attributes, to calculate the similarities between items. In this study, we propose a novel CBF method that uses a multiattribute network to effectively reflect several attributes when calculating correlations to recommend items to users. In the network analysis, we measure the similarities between directly and indirectly linked items. Moreover, our proposed method employs centrality and clustering techniques to consider the mutual relationships among items, as well as determine the structural patterns of these interactions. This mechanism ensures that a variety of items are recommended to the user, which improves the performance. We compared the proposed approach with existing approaches using MovieLens data, and found that our approach outperformed existing methods in terms of accuracy and robustness. Our proposed method can address the sparsity problem and over-specialization problem that frequently affect recommender systems. Furthermore, the proposed method depends only on ratings data obtained from a user's own past information, and so it is not affected by the cold start problem.     

Social group recommendation in the tourism domain

Recommender Systems learn users’ preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual users. In this article, we present a social based approach for recommender systems in the tourism domain, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members of a group. This aspect is a hot research topic in the recommender systems area. In addition, to generate the individual and group recommendations our approach uses a hybrid technique that combines three well-known filtering techniques: collaborative, content-based and demographic filtering. In this way, the disadvantages of one technique are overcome by the others. Our approach was materialized in a recommender system named Hermes, which suggests tourist attractions to both individuals and groups of users. We have obtained promising results when comparing our approach with classic approaches to generate recommendations to individual users and groups. These results suggest that considering the type of users’ relationship to provide recommendations to groups leads to more accurate recommendations in the tourism domain. These findings can be helpful for recommender systems developers and for researchers in this area.     

Surface Reconstruction for Cross Sectional Data

In this paper,a new solution to the problem of reconstructing the surface of 3D objects over a set of cross-sectional contours is proposed.An algorithm for single branch contours connection,which is based on the closest local polar angle method,is first presented.Then the branching problems(including non-singular branchin and singular branching)are completely solved by decomposing them into several single-branching problems.Finally,these methods are applied to the reconstruction of the external surface of a complexly shaped object such as the cellular region of human brain.The results show that the presented methods are practical and satisfactory.     

基于图卷积与外积的协同过滤推荐模型

推荐系统帮助用户主动找到满足其偏好的个性化物品并推荐给用户.协同过滤算法是推荐系统中较为经典的算法,但是其会受到数据冷启动和稀疏性的限制,具有可解释性差和模型泛化能力差等缺点.针对其缺点进行研究,通过将原始的评分矩阵以用户—项目二部图的形式作为输入,将图卷积神经网络设计为一种图自编码器的变体,通过迭代的聚合邻居节点信息得到用户和项目的潜在向量表示,并在其基础上结合卷积神经网络,提出了一种基于卷积矩阵分解的推荐算法,提升了模型的可解释性和泛化能力,同时融合辅助信息也解决了数据的稀疏性问题,并使推荐的性能分别得到了1.4％和1.7％的提升.为今后在基于图神经网络的推荐方向上提供了一种新的思路.