Sequence-based trust in collaborative filtering for document recommendation

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors – time factor and document similarity – in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.     

Efficient watermarking technique for protection and authentication of document images

Multimedia Tools and Applications - In this paper, a hashing based watermarking technique for the protection and authentication of document image is proposed. Message Digest 5 (MD5) hashing is...     

Print-scan invariant text image watermarking for hardcopy document authentication

Multimedia Tools and Applications - In this paper, a novel contour feature-based text image watermarking scheme against print and scan processes is proposed. We employ a mathematical multiplicative...     

视觉文档图像中反渗噪声的去除方法

提出根据直方图特征去除视觉文档图像反渗噪声的方法.对于灰度直方图呈双峰特性的噪声图像,选取适当区域分别进行增强或归一化处理以达到去除反渗噪声的目的;而对于直方图呈单峰特性的含反渗噪声图像,建立了一种基于背景分离的新算法.实验结果表明,该方法可以有效去除视觉文档图像中的反渗噪声.     

Integrating knowledge flow mining and collaborative filtering to support document recommendation

Knowledge is a critical resource that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations must exploit effective and efficient methods of preserving, sharing and reusing knowledge in order to help knowledge workers find task-relevant information. Hence, an important issue is how to discover and model the knowledge flow (KF) of workers from their historical work records. The objectives of a knowledge flow model are to understand knowledge workers’ task-needs and the ways they reference documents, and then provide adaptive knowledge support. This work proposes hybrid recommendation methods based on the knowledge flow model, which integrates KF mining, sequential rule mining and collaborative filtering techniques to recommend codified knowledge. These KF-based recommendation methods involve two phases: a KF mining phase and a KF-based recommendation phase. The KF mining phase identifies each worker’s knowledge flow by analyzing his/her knowledge referencing behavior (information needs), while the KF-based recommendation phase utilizes the proposed hybrid methods to proactively provide relevant codified knowledge for the worker. Therefore, the proposed methods use workers’ preferences for codified knowledge as well as their knowledge referencing behavior to predict their topics of interest and recommend task-related knowledge. Using data collected from a research institute laboratory, experiments are conducted to evaluate the performance of the proposed hybrid methods and compare them with the traditional CF method. The results of experiments demonstrate that utilizing the document preferences and knowledge referencing behavior of workers can effectively improve the quality of recommendations and facilitate efficient knowledge sharing.     

基于UAS模式的馆际互借与文献传递系统认证方式初探

本文通过分析高校数字图书馆馆际互借与文献传递系统用户身份认证方式的发展过程,重点阐述基于CALIS馆际互借系统的UAS联合认证方式.共享了本校图书馆使用UAS联合认证方式的一些体会.     

Algorithms for the Determination of Cutsets in a Hypergraph

Given a hypergraph,this paper provides three algorithms for finding all its minimal cutsets,minimal link cutsets and the least cutsets.The result not only set up a new studying field on cutsets of hypergraph,but also lay a foundation of analyzing the performance of multibus systems.The algorithm for determining all the least cutsets in a hypergaph is polynomial complex and more efficient than that in [2].     

基于兴趣感知和时间因子的个性化菜品推荐 *

目前推荐系统已广泛应用在各种电子商务网站上,但针对菜品的个性化推荐很少。针对菜品推荐中存在别名多、用户菜品矩阵稀疏以及新用户冷启动等难题,对基于用户的协同过滤算法进行改进,设计一种融合专家选择和在线推荐的菜品推荐系统。专家选择通过对菜品进行种类层次划分为用户兴趣建模做准备,在线推荐通过兴趣感知选择算法选择餐厅中的专家用户和候选菜品,从而实现对用户菜品的推荐。最后通过在候选菜品选择时引入时间敏感因子和协同过滤中引入时间遗忘因子,改进兴趣感知算法和菜品偏好预测效果。实验结果表明,所设计算法较传统算法在准确性和推荐效率有明显改进,并得出了针对菜品推荐时引入时间因子有利提高推荐准确性的结论。     

Algorithms of information authentication for the automatic control systems on the basis of structures in finite vector spaces

New algebraic structures differing from the existing ones in new kinds of operations over finite sets of m-dimensional vectors (m ≥ 2) with discrete components were developed to construct information authentication algorithms for the automatic control systems. The operation of vector multiplication including multiplication and summation of the coordinates of the multiplied vectors modulo simple number p was defined. Depending on the structure of module p, dimensionality m, and value of the “extension” coefficients defined in terms of the rules of multiplication of the base vectors, the vector spaces were shown to include high-order finite groups or be extended fields that can be efficiently used to construct effective algorithms of electronic digital signature.     

融合时间综合影响的轮盘赌游走个性化推荐算法

传统的基于图的推荐算法忽略了时间综合信息影响从而导致推荐质量不高。针对这一问题,提出一种融合时间综合影响的轮盘赌游走个性化推荐算法。该算法以用户项目二分图为基础,引入衰减函数,将时间综合信息对推荐的影响量化成图节点的关联概率;然后采用轮盘赌模型根据关联概率选择游走目标;最终对每个用户做出top-N推荐。实验结果表明:该算法比传统基于图的随机游走PersonalRank算法在推荐的准确度、召回率以及覆盖率指标上都有明显提高。     

一种综合标签和时间因素的个性化推荐方法

针对现存的基于标签的社会化推荐系统在构建用户兴趣模型时存在的缺陷,提出一种综合标签及其时间信息的资源推荐(TTRR)模型。此模型考虑了用户的兴趣具有时间性的特点,即用户兴趣是随着时间而变化的、用户最近新打的标签更能反映用户近期的兴趣这一特性。为此,在借鉴协同过滤思想的基础上,通过利用标签使用频率信息和项目的标注时间来构建用户评分伪矩阵;在此基础上计算目标用户的最近邻集合;最后根据邻居用户给出推荐结果。通过在CiteULike数据集上进行实验,并与传统的基于标注的推荐方法进行比较,实验结果表明,TTRR模型能够更好地反映出用户的偏好,能够显著地提高推荐准确度。     

Parameters evaluation of SOLA algorithm for time scale modification

Successful operation of the Synchronous Overlap and Add (SOLA) algorithm for Time Scale Modification (TSM) of speech is closely tied to the proper choice of parameters. This paper investigates the quality of time scale modified speech under different values of primary parameters. Based on Mean Opinion Score (MOS) tests and Bark Spectral Distortion (BSD) measure, the proper choices of synthesis shift (Ss) and the duration of the shift search interval (K _max?) are given experimentally. The conclusions can be helpful for operating the SOLA algorithm for time scale modification of speech.     

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.

     

融合多时间尺度和特征加强的知识图谱推荐模型

针对现有的知识图谱推荐模型没有考虑到用户的周期特征以及待推荐项目会对用户近期兴趣产生影响的问题,提出一种融合多时间尺度和特征加强的知识图谱推荐模型（MTFE）。首先,采用长短期记忆（LSTM）网络在不同时间尺度上挖掘用户的周期特征并融入到用户表示中;然后,通过注意力机制挖掘待推荐项目中与用户近期特征相关性较强的特征,将其加强后融入项目表示中;最后,通过评分函数计算用户对待推荐项目的评分。在真实数据集Last.FM、MovieLens-1M和MovieLens-20M上把所提模型和个性化实体推荐（PER）、协同知识嵌入（CKE）、LibFM、RippleNet、知识图卷积网络（KGCN）、协同知识感知注意网络（CKAN）等知识图谱推荐模型进行对比。实验结果表明,在三个数据集上MTFE相较于表现最优的对比模型的F1性能分别提升了0.78、1.63和1.92个百分点,AUC指标在三个数据集上分别提升了3.94、2.73和1.15个百分点。可见,所提模型相较于对比图谱推荐模型有更好的推荐效果。     

Adaptive modification of time evolving meshes

The accuracy of a finite element computation depends on the mesh size and the distribution of mesh points. Adapting the mesh to capture the salient features in the finite element computation can provide a much more accurate solution without imposing an excessive increase in mesh size and computational cost. When the shape of the meshed domain is evolving in time mesh adaptation is essential in order to maintain conformity of the mesh with the domain boundaries. Mesh adaptation for time evolving domains can be accomplished through a combination of mesh movement with mesh modification by selective coarsening and enrichment of various regions in the mesh. Large deformations of the computational domain and boundary can be accommodated if overall mesh quality is maintained after each adaptation cycle.     

结合用户特征分类和动态时间的协同过滤推荐

针对推荐系统中数据稀疏、冷启动和用户特征动态变化及不同用户对同一特征依赖程度不同等问题的影响,提出了结合用户特征分类和动态时间的协同过滤推荐。考虑用户特征动态变化的同时将用户依据特征分类以弥补计算相似度的不足,并将用户特征和用户评分相结合解决冷启动问题。结果表明该算法能有效提高推荐质量。