Performance improvement for recommender systems using ontology

Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (CF). In hybrid recommendation systems that combine CF and CBF, the CF part uses two methods, including memory- and model-based approaches. Both approaches have some advantages and disadvantages for item recommendation. Sparsity has been one of the main difficulties associated with these approaches, whereas recommendation with high accuracy has been one of the important advantages of the memory-based approach. However, this approach is not scalable for current recommendation systems as their databases include huge numbers of items and users. In contrast, the model-based approach generates recommendations with low accuracy but is scalable for large databases in e-commerce recommender systems. Accordingly, to address this problem and take advantage of both approaches, in this work, we propose a new hybrid recommendation method and evaluate it using a real-world dataset. The aim is to improve efficiency and accuracy by designing a heuristic hybrid recommender method that combines memory-based and model-based approaches. Specifically, we use ontology in the CF part and improve ontology structure by eliminating uniformity of edges of the hierarchical relation between concepts (IS-A relation) in item ontology in the CBF part. Ontology structure is considered for improving accuracy; according to this, a new method for measuring semantic similarity that is more accurate than the traditional methods is presented. This new method can enhance the accuracy of CF and CBF in our method. In addition, the number of searches required to find similar clusters and neighbor users of the target user is decreased significantly using ontology, enhanced clustering and the new proposed algorithm. We evaluate the proposed method using a real-world dataset. The experimental results show that our method is more scalable and accurate than the benchmark k-Nearest Neighbor (k-NN) and model-based recommendation methods.     

形状文法审美下的七曲山文昌文化之纹样衍生设计研究

目的 为促进七曲山景区文昌文化和景区自然资源的可持续性发展,提高景区文化宣传价值和经济价值,建立景区文化自信,提出一种基于形状文法的纹样衍生设计方案。该方案既包含形状文法的秩序之美,又融合景区的自然特征,可与景区相关产品设计进行有机融合,形成特色文创产品。方法 筛选、整理、分析素材,形成感性词汇。结合文昌文化特征元素及关联传统纹样设计图元因子,并运用用户感知评价、层次分析矩阵等进行图元因子排序。随后,运用形状文法设计纹样创新图案,以相关文创产品进行设计验证。结论 纹样方案包含七曲山景区特征,拓展了形状文法审美外延,形成秩序与自由的对比之美。该方案可有机融合于景区文创产品设计中,从而有效宣传景区文化,提高旅游和经济价值。     

Interactions: Dialogues on Body,Protections and Derivatives

Could kinetic clothing be the next big thing? A collaboration between Dutch fashion designer Iris van Herpen and Italy-based architect and educator Niccolò Casas has produced a series of dresses that blur the boundaries between the body and the space around it. Casas explains how they have fused handcraft with technology, using 3D printing, mass customisation techniques, and morpho-dynamic simulations, among other means, to produce couture pieces that react to bodily movement in extraordinary new ways.     

服装聚酯热熔胶性能讨论

服装聚酯热熔胶的制造配方及工艺的复杂性,使其性能指标的调节和控制具有一定难度,结合有关理论,总结了在生产实践中积累的有关这方面的经验。     

高级女装衣身结构分析与探索

利用立体裁剪的手法，分析胸腰的造型，探索腰省位置、大小的比例关系，为高级女装衣身结构建立了科学的基础。     

异形滚柱的在线电解修型成型磨削工艺试验研究

介绍了一种用于汽车和摩托车上先进超越离合器异形滚柱的在线电解成型(ELID)磨削加工工艺试验及相关设备与装置,主要包括成型磨削的工艺实施方案,在平面磨床上实现成型磨削的工装设计及在线电解砂轮修型的装置设计与布局等问题。理论上分析了ELID成型磨削在加工中能按照理想的成型曲线自动修正砂轮母线的形状,从而达到很高的形状精度和较低的表面粗糙度。总结了影响加工质量和效率的因素和规律;同原来的成型拉削相比,采用成型磨削具有效率、质量与稳定性、工具成本和管理等方面的诸多优势。     

孕妇装样板设计中的两个问题

利用人体简化模型 ,对目前孕妇装中普遍存在的前中心起吊和侧缝偏转问题作了分析 ,并提出了解决方法。     

试论高职高专服装设计应用型人才的培养

高职高专服装教育在服装教育体系中占有重要地位。服装应用型人才的培养如何定位是形成高校办学特色、取得竞争优势的关键。分析高职高专服装教育人才培养的现状 ,探讨应用型人才的培养途径 ,解决高校教育与企业需求矛盾是深化教育改革的重要课题。     

基于面料肌理设计的礼服设计研究

叙述了面料肌理的概念及其分类,分析了礼服设计中面料肌理的表现技法,阐述了面料肌理设计对礼服设计的影响,为服装设计师提供了设计的新思路.     

树皮服饰起源背景初探

探讨了树皮服饰的起源以及其在人类服饰发展史上的影响和意义.树皮服饰是在特定的社会历史条件下产生的,它真实地反映了穿着者所处的自然环境、社会环境和文化环境,同时也反映出穿着者自身的价值取向、审美追求等内在特点.