Social media filtering based on collaborative tagging in semantic space

We propose a semantic collaborative filtering method to enhance recommendation quality derived from user-generated tags. Social tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of semantic tagging for ambiguity, synonymy, and semantic interoperability, which are notable challenges in information filtering. The proposed approach first determines semantically similar users using social tagging and subsequently discovers semantically relevant items for each user. Experimental results show that our method offers significant advantages both in terms of improving the recommendation quality and in dealing with ambiguity, synonymy, and interoperability issues.     

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.     

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.     

微博中用户标签的研究

微博环境中用户可以为自己添加标签,用户所添加的标签往往被视为是对自身特点和兴趣的重要描述信息.标签中所包含的信息可能有助于建立精确的用户描述,因此在个性化推荐、专家检索、影响力分析等应用中有潜在的应用价值.首先,在大规模数据上分析和研究了微博中用户添加标签的行为及标签内容分布的特点;之后,通过主题模型对用户的微博内容进行分析,实验结果表明:用户的标签越相似,微博内容也越相似,反之亦然;随后,分析了用户关注关系与微博和标签内容之间的联系,实验结果显示,有关注关系的用户之间微博和标签的内容越相似;基于这个发现,分别使用标签内容和微博内容对真实微博数据中的用户关注关系进行预测,结果表明:基于标签的预测方法其效果明显优于基于微博内容的预测方法,显示出用户标签在描述用户兴趣方面的价值.     

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.     

An expressive framework and efficient algorithms for the analysis of collaborative tagging

The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three components, user, item (e.g., photos in Flickr), and tags (i.e., words or phrases). Analyzing how tags are assigned by certain users to certain items has important implications in helping users search for desired information. In this paper, we develop a dual mining framework to explore tagging behavior. This framework is centered around two opposing measures, similarity and diversity, applied to one or more tagging components, and therefore enables a wide range of analysis scenarios such as characterizing similar users tagging diverse items with similar tags or diverse users tagging similar items with diverse tags. By adopting different concrete measures for similarity and diversity in the framework, we show that a wide range of concrete analysis problems can be defined and they are NP-Complete in general. We design four sets of efficient algorithms for solving many of those problems and demonstrate, through comprehensive experiments over real data, that our algorithms significantly out-perform the exact brute-force approach without compromising analysis result quality.     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions

Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.     

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.     

A social tag clustering method based on common co-occurrence group similarity

Social tagging systems are widely applied in Web 2.0. Many users use these systems to create, organize, manage, and share Internet resources freely. However, many ambiguous and uncontrolled tags produced by social tagging systems not only worsen users’ experience, but also restrict resources’ retrieval efficiency. Tag clustering can aggregate tags with similar semantics together, and help mitigate the above problems. In this paper, we first present a common co-occurrence group similarity based approach, which employs the ternary relation among users, resources, and tags to measure the semantic relevance between tags. Then we propose a spectral clustering method to address the high dimensionality and sparsity of the annotating data. Finally, experimental results show that the proposed method is useful and efficient.     

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.     

Tag recommendation by machine learning with textual and social features

Tags are very popular in social media (like Youtube, Flickr) and provide valuable and crucial information for social media. But at the same time, there exist a great number of noisy tags, which lead to many studies on tag suggestion and recommendation for items including websites, photos, books, movies, and so on. The textual features of tags, likes tag frequency, have mostly been used in extracting tags that are related to items. In this paper, we address the problem of tag recommendation for social media users. This issue is as important as the tag recommendation for items, because the tags representing users are strongly related to the users’ favorite topics. We propose several novel features of tags for machine learning that we call social features as well as textual features. The experimental results of Flickr show that our proposed scheme achieves viable performance on tag recommendation for users.     

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。     

标签扩展的协同过滤推荐算法

大多数利用标签与用户和项目之间关系的推荐算法,都要面临用户个体不同所导致的标签稀疏问题,不同的用户为项目所标注的标签会有所不同.针对由于用户标注标签的随意性而导致的用户标签和项目标签矩阵稀疏问题,提出了一种标签扩展的协同过滤推荐算法.该算法根据用户标注标签的行为计算基于标签的标签相似度,根据用户标注的标签语义计算基于标签语义的标签相似度,从用户行为和标签语义2个方面评估标签的相似度,并利用标签相似度来扩展每个项目标签,降低由项目与标签的关联关系产生的矩阵稀疏度.在M ovieLens数据集上的实验结果表明,所提算法在精度上有所提高.     

Summarizing dynamic Social Tagging Systems

Social tagging is a popular method that allows users of social networks to share annotation in the form of keywords, called tags, assigned to resources. Social tagging addresses information overload by easing the task of locating interesting entities in a social network. Nevertheless, users can still be overwhelmed by too many tags posted at each moment. A process is needed that offers an accurate overview of the representative entities and their relationships with each other, while dealing with the dynamics of social tagging and of tags’ semantics. We propose a method for the automated summarization of an evolving multi-modal social network, focusing on the entities that stay representative over time for some subnetwork in the social tagging system. We report on experiments with real data from the Bibsonomy social tagging system, where we compare our dynamic approach with a static one.     

基于对抗采样的社交推荐算法

推荐系统利用用户的历史记录、物品的基础信息等数据进行建模来捕获用户的偏好,有效缓解了信息过载等问题,虽然其已应用广泛,但整个推荐领域面临的挑战却依旧存在,其中数据稀疏这一问题对于推荐性能有举足轻重的影响。近年来,大量研究表明基于社交信息的推荐算法能够有效缓解数据稀疏问题,但它们也仍然存在一定的局限。线上的社交网络是非常稀疏的,并且线上社交网络中的“朋友”通常包括同学、同事、亲戚等,因此,拥有显式朋友关系的用户不一定拥有相似的偏好,即直接利用显式朋友的兴趣偏好进行推荐会存在噪声问题。此外,大部分基于隐式反馈的算法通常直接对用户没有交互过的物品进行随机采样,然后将其作为用户实际交互过的物品的负样本来优化模型,然而用户没有交互过的物品并不代表用户不喜欢,这种粗粒度的采样策略忽略了用户的真实偏好,同样也带来了一定程度的噪声。生成对抗网络（GANs）因其在训练中捕获复杂数据分布的能力以及强大的鲁棒性被广泛应用到推荐系统中,为了减弱上述噪声问题带来的影响,本文基于生成对抗网络提出了一种细粒度的对抗采样推荐模型（ASGAN）,包括一个生成器和判别器。其中,生成器首先利用图表示学习技术初始化社交网络,接着为用户生成一个与其偏好相似的朋友,然后再从该朋友喜欢的物品集中同时生成该用户喜欢的物品和用户不喜欢的物品。判别器则尽可能区分出用户实际交互过的物品和生成器生成的两类物品。随着对抗训练的进行,生成器能更有效地进行社交朋友采样和物品采样,而判别器能够良好地捕获用户的真实偏好分布。最后,在三个公开的真实数据集上与现有的六个工作进行对比,实验结果证明：ASGAN拥有更好的推荐性能,通过重构社交网络和细粒度采样有效缓解了社交信息和物品采样策略带来的噪声问题。     

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.     

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.     

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.     

基于标签聚类与项目主题的协同过滤推荐算法

传统基于项目的协同过滤算法在计算项目相似度时仅依靠评分数据,未考虑项目的自身特征。社会化标注的出现使得标签能在一定程度上反映项目特征,但标签具有语义模糊的特点,因此直接将标签纳入协同过滤算法存在一定问题。为解决上述问题,提出一种改进的基于项目的协同过滤推荐算法。该算法对标签进行聚类并生成主题标签簇,根据项目标注情况计算项目与主题间的相关度并生成项目-主题相关度矩阵,同时将其与项目-评分矩阵相结合来计算项目间的相似度,采用协同过滤完成对目标项目的评分预测,以实现个性化推荐。在Movielens数据集上的实验结果表明,该算法能够解决标签的语义模糊问题并提升推荐质量。