Topic-based ranking in Folksonomy via probabilistic model

Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. Most social tagging systems order tags just according to the input sequence with little information about the importance and relevance. This limits the applications of tags such as information search, tag recommendation, and so on. In this paper, we pay attention to finding the authority score of tags in the whole tag space conditional on topics and put forward a topic-sensitive tag ranking (TSTR) approach to rank tags automatically according to their topic relevance. We first extract topics from folksonomy using a probabilistic model, and then construct a transition probability graph. Finally, we perform random walk over the topic level on the graph to get topic rank scores of tags. Experimental results show that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into tag recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.     

Personalized topic-based tag recommendation

More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text-based information retrieval. To support the user in choosing the right keywords, tag recommendation algorithms have emerged. In this setting, not only the content is decisive for recommending relevant tags but also the user's preferences.In this paper we introduce an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user. As models we investigate simple language models as well as Latent Dirichlet Allocation. Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation, and our approach significantly outperforms state-of-the-art approaches.     

Mining multi-tag association for image tagging

Automatic media tagging plays a critical role in modern tag-based media retrieval systems. Existing tagging schemes mostly perform tag assignment based on community contributed media resources, where the tags are provided by users interactively. However, such social resources usually contain dirty and incomplete tags, which severely limit the performance of these tagging methods. In this paper, we propose a novel automatic image tagging method aiming to automatically discover more complete tags associated with information importance for test images. Given an image dataset, all the near-duplicate clusters are discovered. For each near-duplicate cluster, all the tags occurring in the cluster form the cluster’s “document”. Given a test image, we firstly initialize the candidate tag set from its near-duplicate cluster’s document. The candidate tag set is then expanded by considering the implicit multi-tag associations mined from all the clusters’ documents, where each cluster’s document is regarded as a transaction. To further reduce noisy tags, a visual relevance score is also computed for each candidate tag to the test image based on a new tag model. Tags with very low scores can be removed from the final tag set. Extensive experiments conducted on a real-world web image dataset—NUS-WIDE, demonstrate the promising effectiveness of our approach.     

Integrating ontology technology with folksonomies for personalized social tag recommendation

Social tagging systems leverage social interoperability by facilitating the searching, sharing, and exchanging of tagging resources. A major drawback of existing social tagging systems is that social tags are used as keywords in keyword-based search. They focus on keywords and human interpretability rather than on computer interpretable semantic knowledge. Therefore, social tags are useful for information sharing and organizing, but they lack the computer-interpretability needed to facilitate a personalized social tag recommendation. An interesting issue is how to automatically generate a personalized social tag recommendation list to users when a resource is accessed by users. The novel solution proposed in this study is a hybrid approach based on semantic tag-based resource profile and user preference to provide personalized social tag recommendation. Experiments show that the Precision and Recall of the proposed hybrid approach effectively improves the accuracy of social tag recommendation.     

基于潜在标签挖掘和细粒度偏好的个性化标签推荐

为进一步提高个性化标签推荐性能,针对标签数据的稀疏性以及传统方法忽略隐藏在用户和项目上下文中潜在标签的缺陷,提出一种基于潜在标签挖掘和细粒度偏好的个性化标签推荐方法。首先,提出利用用户和项目的上下文信息从大量未观测标签中挖掘用户可能感兴趣的少量潜在标签,将标签重新划分为正类标签、潜在标签和负类标签三类,进而构建〈用户,项目〉对标签的细粒度偏好关系,在缓解标签稀疏性的同时,提高对标签偏好关系的表达能力;然后,基于贝叶斯个性化排序优化框架对细粒度偏好关系进行建模,并结合成对交互张量分解对偏好值进行预测,构建细粒度的个性化标签推荐模型并提出优化算法。对比实验表明,提出的方法在保证较快收敛速度的前提下,有效地提高了个性化标签的推荐准确性。     

Improving image tags by exploiting web search results

Automatic image tagging automatically assigns image with semantic keywords called tags, which significantly facilitates image search and organization. Most of present image tagging approaches are constrained by the training model learned from the training dataset, and moreover they have no exploitation on other type of web resource (e.g., web text documents). In this paper, we proposed a search based image tagging algorithm (CTSTag), in which the result tags are derived from web search result. Specifically, it assigns the query image with a more comprehensive tag set derived from both web images and web text documents. First, a content-based image search technology is used to retrieve a set of visually similar images which are ranked by the semantic consistency values. Then, a set of relevant tags are derived from these top ranked images as the initial tag set. Second, a text-based search is used to retrieve other relevant web resources by using the initial tag set as the query. After the denoising process, the initial tag set is expanded with other tags mined from the text-based search result. Then, an probability flow measure method is proposed to estimate the probabilities of the expanded tags. Finally, all the tags are refined using the Random Walk with Restart (RWR) method and the top ones are assigned to the query images. Experiments on NUS-WIDE dataset show not only the performance of the proposed algorithm but also the advantage of image retrieval and organization based on the result tags.     

Image tag recommendation based on friendships

The tagging systems have been studied by many researchers in the past decade. Tagging methods have been widely used on the web for searching and recommending images. Social tags are the keywords annotated by users to the images, which contains the information for searching and classifying the images. Tag recommendation system allows mitigating the individual preferences to annotate and recommender images. However, irrelevant and noise tags are frequently included in tags. In this paper, we propose image tag recommendation based on the friends’ relationships in social network (TRboFS) to recommender tags for a new image, both the tags assigned to the favorite images and the friendships of the users who upload the image are employed to predict the tags of the images. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.     

融合关系与内容分析的社会标签推荐

标签是Web 2.0时代信息分类与索引的重要方式.为解决标签系统所面临的不一致性、冗余性以及完备性等问题,标签推荐通过提供备选标签的方法来提高标签的质量.为了进一步提升标签推荐的质量,提出了一种基于标签系统中对象间关系与资源内容融合分析的标签推荐方法,给出了基于LDA(latent Dirichlet allocation)的融合表示对象间关系与资源内容的标签系统生成模型TSM/Forc,提出了一种基于概率的标签推荐方法,并给出了基于吉布斯(Gibbs)抽样的参数估计方法.实验结果表明,该方法可以提供比当前主流与最新方法更加准确的推荐结果.     

校园社交网络中社会化标签系统的研究与实现

为使校园社交网络能够准确、灵活、高效地处理大量各种类型的标签信息,研究并实现了基于校园社交网络的社会化标签系统。提出了使用五元组表示的、包含资源标签和实体标签的标签标注模型,完成了基于校园社交网络的社会化标签系统的总体设计、功能设计和数据库设计,设计了由标注对象所有者、标签标注者、校园社交网络中的关系网络与隐私设置以及标签的使用方式决定的标签标注权限与使用权限,建立了针对不同标记对象的特点使用不同标签推荐算法的标签推荐策略,最后讨论了以标签云为主的标签展示方式。     

Content-based and collaborative techniques for tag recommendation: an empirical evaluation

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users’ mental model; secondly, the absence of a controlled vocabulary reduces the users’ learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.     

融合主题与语言模型的个性化标签推荐方法研究

随着Web的推广和普及,产生了越来越多的网络数据。 广泛应用了 标签系统 ,以便人们使用搜索技术来组织和使用这些信息。这些数据允许用户使用关键字(标签)注释资源,为传统的基于文本的信息检索提供了方案。为了支持用户选择正确的关键字,标签推荐算法应运而生。提出了一种个性化标签推荐方法,该方法综合了用户的资源标签与标签概率模型。该模型利用了简单语言模型和隐含狄利克雷分配模型,并针对现实世界的大型数据集进行了大量实验。实验表明,该个性化方法改进了标签推荐算法,推荐结果优于传统方法。     

Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.     

A Review of Ontology‐Based Tag Recommendation Approaches

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology‐based recommender systems is favorably compared to other systems in the literature.     

Evolutionary taxonomy construction from dynamic tag space

Collaborative tagging becomes a common feature of current web sites, facilitating ordinary users to annotate and represent online resources. The large collection of tags and their relationships form a tag space. In this kind of tag space, the popularity and correlation amongst tags capture the current social interests. Tags are freely chosen keywords and difficult to organize. As a hierarchical concept structure to represent the subsumption relationships, automatically extracted taxonomies become a viable method to manage collaborative tags. However, tags change over time, and it is also imperative to incorporate the temporal tag evolution into the extracted taxonomies. In this paper, we formalize the problem of evolutionary taxonomy generation over a large collection of tags. A line of taxonomies are generated to reflect the temporal changes of underlying tag space. The proposed evolutionary taxonomy framework consists of two novel contributions. First, we develop a context-aware edge selection algorithm for taxonomy extraction. This method is built on seminal association-rule mining algorithm. Second, we propose several strategies for evolutionary taxonomy fusion, which smooths the newly generated taxonomy with prior ones. We conduct an extensive performance study using a large real-life web page tagging dataset (i.e., Del.ici.ous). The empirical results clearly verify the effectiveness and efficiency of the proposed approach.     

Tag recommendation method in folksonomy based on user tagging status

A folksonomy consists of three basic entities, namely users, tags and resources. This kind of social tagging system is a good way to index information, facilitate searches and navigate resources. The main objective of this paper is to present a novel method to improve the quality of tag recommendation. According to the statistical analysis, we find that the total number of tags used by a user changes over time in a social tagging system. Thus, this paper introduces the concept of user tagging status, namely the growing status, the mature status and the dormant status. Then, the determining user tagging status algorithm is presented considering a user’s current tagging status to be one of the three tagging status at one point. Finally, three corresponding strategies are developed to compute the tag probability distribution based on the statistical language model in order to recommend tags most likely to be used by users. Experimental results show that the proposed method is better than the compared methods at the accuracy of tag recommendation.     

Tagging image by merging multiple features in a integrated manner

Image tagging is a task that automatically assigns the query image with semantic keywords called tags, which significantly facilitates image search and organization. Since tags and image visual content are represented in different feature space, how to merge the multiple features by their correlation to tag the query image is an important problem. However, most of existing approaches merge the features by using a relatively simple mechanism rather than fully exploiting the correlations between different features. In this paper, we propose a new approach to fusing different features and their correlation simultaneously for image tagging. Specifically, we employ a Feature Correlation Graph to capture the correlations between different features in an integrated manner, which take features as nodes and their correlations as edges. Then, a revised probabilistic model based on Markov Random Field is used to describe the graph for evaluating tag??s relevance to query image. Based on that, we design an image tagging algorithm for large scale web image dataset. We evaluate our approach using two large real-life corpuses collected from Flickr, and the experimental results indicate the superiority of our proposed approach over state-of-the-art techniques.     

基于异构网络面向多标签系统的推荐模型研究

标签成为信息组织的重要方式之一,随着推荐系统的蓬勃发展,标签推荐成为学者们研究的重要问题之一.目前存在各种各样的标签系统,其功能千差万别,标签数据信息越来越复杂.目前研究往往针对特定类型标签数据,缺乏既综合考虑标签数据中不同类型对象的复杂信息又能适用于多种标签系统数据的标签推荐模型.构建了标签推荐模型HnMTR,该模型首先针对标签数据中不同类型对象构建异构网络模型,其次对异构网络模型中不同类型顶点进行同空间映射,使不同类型的顶点和边可在同一空间进行量化比较;最后基于同空间映射后网络,引入多参数马尔可夫模型进行标签评分和推荐.通过基于豆瓣、Delicious和Meetup这3个标签系统数据实验,其结果表明,HnMTR模型平均准确率比目前主流算法提高10%以上,取得了较好的推荐结果.     

A framework for tag-aware recommender systems

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.