Folksonomy‐Based Recommender Systems: A State‐of‐the‐Art Review

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open‐ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy‐based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link‐analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.     

Resource recommendation in social annotation systems: A linear-weighted hybrid approach

Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility.     

中文网页语义标注:由句子到RDF表示

语义网远景的实现需要自动化的语义标注方法,提出了一种在领域本体指导下,针对中文网页的语义标注方法,运用统计学方法与自然语言处理技术,以文档中句子为处理对象,采取识别和组合两个阶段来完成句子向RDF表示的映射,它具有以下特点:以统计方法获得领域相关词汇,构造领域词汇标注列表作为外部领域知识,降低对通用语言本体的依赖;显式的属性类型标注方法识别出句子中表达关系的词汇,标注为属性类型,利于后续关系抽取;构造句子的句法依存关系树(森林),按照依存关系对词汇进行组合,形成RDF陈述.实验结果显示此方法较基于主谓宾语法关系的语义标注方法更为有效.     

一种基于社会化标注的网页检索方法

利用社会化标注对网页检索进行改进,提出一种加权的社会化SimRank算法。从社会化标注系统中提取网页以及标签词之间的相似度信息。分别用这2类相似度信息来计算网页本身的质量同网页与查询之间的相关性。依据网页的质量和相关性信息对网页进行重排序。在del.icio.us网站抽取真实标注数据集进行实验,结果表明,该方法挖掘到的信息能够较好地改善网页检索效果。     

一种基于社会性标注的网页排序算法

社会性标注作为一种新的资源管理和共享方式,吸引为数众多的用户参与其中,由此产生的大量社会性标注数据成为网页质量评价的一个新维度.文中研究如何利用社会性标注改进网页检索性能,提出一种有机结合网页和用户的查询相关性与互增强关系的网页排序算法.首先利用统计主题模型,使用相关标签为网页和用户建模,并计算查询相关性.然后利用二部图模型刻画网页和用户间的互增强关系,并使用相关标签与用户兴趣和网页内容的匹配度为互增强关系赋予权重.最后结合查询相关性和互增强关系,以迭代方式同时计算网页和用户的评分.实验结果表明,文中提出的检索模型和互增强模型能够有效地提高排序算法的性能.与目前的代表性算法相比,该算法在检索性能上有明显提高.     

Folksonomy-based personalized search and ranking in social media services

In recent years, social Web users have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help such users retrieve useful social media content, we propose a new model of tag-based personalized searches to enhance not only retrieval accuracy but also retrieval coverage. By leveraging social tagging as a preference indicator, we build two models: (i) a latent tag preference model that reflects how a certain user has assigned tags similar to a given tag and (ii) a latent tag annotation model that captures how users have tagged a certain tag to resources similar to a given resource. We then seamlessly map the tags onto items, depending on an individual user's query, to find the most desirable content relevant to the user's needs. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the art algorithms and show our method's feasibility for personalized searches in social media services.     

基于特征项的群组信息推荐算法

个性化推荐系统采用知识发现技术给用户提供准确、合理的信息从而赢得客户。基于用户群组特征的推荐方式是当前在研究和实用两方面都取得一定成功的一种模式,但是这种算法的复杂度随着用户数量的增加而急剧增长,因此在实际的应用中,面对着数以万计的用户,服务系统要承担大负荷的计算量,从而导致推荐效率的下降。该文提出了一种基于特征项的推荐算法来解决基于用户的推荐算法所面临的可扩展性差的问题。实验表明,使用基于特征项的推荐算法能够在提高推荐效率的同时,达到或者超越基于用户的推荐算法的推荐性能。     

基于社会标注质量的文本分类模型框架

社会标注是一种用户对网络资源的大众分类,蕴含了丰富的语义信息,因此将社会标注应用到信息检索技术中有助于提高信息检索的质量。研究了一种基于社会标注的文本分类改进算法以提高网页分类的效果。由于社会标注属于大众分类,标注的产生具有很大的随意性,标注的质量差别很大,因此首先利用文档间的语义相似度以及标注间的语义相似度来对标注的质量进行量化评估。在此基础上对标注进行质量过滤,利用质量相对较好的标注对文档向量空间模型进行扩展,将文档表示成由文档单词以及文档标注信息组成的扩展向量。同时采用支持向量机分类算法进行分类实验。实验结果表明,通过对标注进行质量评估并过滤质量差的标注,同时结合文档内容以及标注来对文档能提高分类的效果,同传统的基于文档内容的分类算法相比,分类结果的F1度量值提高了6.2%。     

The LEMO annotation framework: weaving multimedia annotations with the web

Cultural institutions and museums have realized that annotations contribute valuable metadata for search and retrieval, which in turn can increase the visibility of the digital items they expose via their digital library systems. By exploiting annotations created by others, visitors can discover content they would not have found otherwise, which implies that annotations must be accessible and processable for humans and machines. Currently, however, there exists no widely adopted annotation standard that goes beyond specific media types. Most institutions build their own in-house annotation solution and employ proprietary annotation models, which are not interoperable with those of other systems. As a result, annotation data are usually stored in closed data silos and visible and processable only within the scope of a certain annotation system. As the main contribution of this paper, we present the LEMO Annotation Framework. It (1) provides a uniform annotation model for multimedia contents and various types of annotations, (2) can address fragments of various content-types in a uniform, interoperable manner and (3) pulls annotations out of closed data silos and makes them available as interoperable, dereferencable Web resources. With the LEMO Annotation Framework annotations become part of the Web and can be processed, linked, and referenced by other services. This in turn leads to even higher visibility and increases the potential value of annotations.