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.     

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。     

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.     

Div-clustering: Exploring active users for social collaborative recommendation

Collaborative recommendation (CR) is a popular method of filtering items that may interest social users by referring to the opinions of friends and acquaintances in the network and computer applications. However, CR involves a cold-start problem, in which a newly established recommender system usually exhibits low recommending accuracy because of insufficient data, such as lack of ratings from users. In this study, we rigorously identify active users in social networks, who are likely to share and accept a recommendation in each data cluster to enhance the performance of the recommendation system and solve the cold-start problem. This novel modified CR method called div-clustering is presented to cluster Web entities in which the properties are specified formally in a recommendation framework, with the reusability of the user modeling component considered. We improve the traditional k-means clustering algorithm by applying supplementary works such as compensating for nominal values supported by the knowledge base, as well as computing and updating the k value. We use the data from two different cases to test for accuracy and demonstrate high quality in div-clustering against a baseline CR algorithm. The experimental results of both offline and online evaluations, which also consider in detail the volunteer profiles, indicate that the CR system with div-clustering obtains more accurate results than does the baseline system.     

Automated Semantic Analysis of Schematic Data

Content in numerous Web data sources, designed primarily for human consumption, are not directly amenable to machine processing. Automated semantic analysis of such content facilitates their transformation into machine-processable and richly structured semantically annotated data. This paper describes a learning-based technique for semantic analysis of schematic data which are characterized by being template-generated from backend databases. Starting with a seed set of hand-labeled instances of semantic concepts in a set of Web pages, the technique learns statistical models of these concepts using light-weight content features. These models direct the annotation of diverse Web pages possessing similar content semantics. The principles behind the technique find application in information retrieval and extraction problems. Focused Web browsing activities require only selective fragments of particular Web pages but are often performed using bookmarks which fetch the contents of the entire page. This results in information overload for users of constrained interaction modality devices such as small-screen handheld devices. Fine-grained information extraction from Web pages, which are typically performed using page specific and syntactic expressions known as wrappers, suffer from lack of scalability and robustness. We report on the application of our technique in developing semantic bookmarks for retrieving targeted browsing content and semantic wrappers for robust and scalable information extraction from Web pages sharing a semantic domain. This work has been conducted while the author was at Stony Brook University.     

融合社交信息的局部潜在空间推荐方法

随着社交网络的发展,越来越多的研究利用社交信息来改进传统推荐算法的性能,然而现有的推荐算法大多忽略了用户兴趣的多样化,未考虑用户在不同社交维度中关心的层面不同,导致推荐质量较差.为了解决这个问题,提出了一种同时考虑全局潜在因子和不同子集特定潜在因子的推荐方法LSFS,使得推荐过程既考虑了用户共享偏好又考虑了用户在不同子集中的特定偏好.考虑到参与到不同社交维度的用户对不同的项目感兴趣,首先根据用户的社交关系将用户划分到不同的子集中;其次通过截断奇异值分解技术建模用户对项目的评分,其中全局潜在因子捕获用户共享的层面,而不同用户子集的特定潜在因子捕获用户关心的特定层面;最后,结合全局与局部潜在因子预测用户对未评分项目的评分.实验结果表明该方法可行且有效.     

基于Web日志的煤矿企业网站个性化推荐服务研究

为了解决煤矿企业网站用户查找信息难的问题,提出了一种基于Web日志的煤矿企业网站个性化推荐服务模型。该模型应用关联规则对新用户进行页面推荐,应用聚类算法对老用户进行页面推荐;并结合点击网页的次数、网页的浏览时间、雅可系数与最长公共路径系数来度量用户兴趣度的方法,可为用户准确地推荐其感兴趣的页面。测试结果表明,该模型能够有效地对网页资源进行分类并进行个性化推荐。     

一种基于空间变换的协同过滤推荐算法

传统的协同过滤推荐算法在实际应用中往往面临着计算可扩展性的问题。为解决此问题,文中在基于物品的协同过滤推荐的框架下,通过融合社交关系信息,提出了一种基于空间变换的协同过滤推荐算法。首先,根据用户社交网络信息,运用社区发现算法将用户划分为不同的类;其次,基于评分信息,根据用户和物品之间的对应关系找到各个用户类所对应的物品类;最后,通过各个物品对每一物品类的隶属关系,将稀疏的高维评分信息矩阵转换为一个低维稠密的物品隶属度矩阵,进而基于该矩阵进行相似度计算并进行协同过滤推荐。在公开数据集上将所提方法与其他算法进行了对比实验分析,结果表明,所提算法能够在保证推荐准确性的同时明显提升计算效率。     

Relevance measures for the creation of groups in an annotation system

The MADCOW annotation system supports a notion of group, facilitating focused annotations with respect to a domain. In previous work, we adopted ontologies to represent knowledge about domains, thus allowing more refined annotations to a group, and discussed how the use of ontologies facilitates the formulation of semantically significant queries for retrieving annotations on specific topics. We now expand on previous results and study two new types of measures to identify matches between users׳ interests and groups: Degree Centrality, developed for social networks to assess the quality of concepts in an ontology, and URL concordance, indicating the similarity of interests among users who annotate the same pages.     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for products or services during a live interaction. These systems, especially collaborative filtering based on user, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the kinds of commodity to Web sites in recent years poses some key challenges for recommender systems. One of these challenges is ability of recommender systems to be adaptive to environment where users have many completely different interests or items have completely different content (We called it as Multiple interests and Multiple-content problem). Unfortunately, the traditional collaborative filtering systems can not make accurate recommendation for the two cases because the predicted item for active user is not consist with the common interests of his neighbor users. To address this issue we have explored a hybrid collaborative filtering method, collaborative filtering based on item and user techniques, by combining collaborative filtering based on item and collaborative filtering based on user together. Collaborative filtering based on item and user analyze the user-item matrix to identify similarity of target item to other items, generate similar items of target item, and determine neighbor users of active user for target item according to similarity of other users to active user based on similar items of target item.In this paper we firstly analyze limitation of collaborative filtering based on user and collaborative filtering based on item algorithms respectively and emphatically make explain why collaborative filtering based on user is not adaptive to Multiple-interests and Multiple-content recommendation. Based on analysis, we present collaborative filtering based on item and user for Multiple-interests and Multiple-content recommendation. Finally, we experimentally evaluate the results and compare them with collaborative filtering based on user and collaborative filtering based on item, respectively. The experiments suggest that collaborative filtering based on item and user provide better recommendation quality than collaborative filtering based on user and collaborative filtering based on item dramatically.     

A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the Kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.