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.     

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.     

一种融合社会化标注系统中主题域相似的个性化排序方法

随着网络技术的发展,互联网中越来越多的资源被应用于信息检索中,大量的研究表明,社会化标注可以用于改善信息检索。现有个性化排序的方法中,用户之间的相似度大多通过其共同使用过的标签集来计算。然而,现实中用户标注数据存在稀疏性和标签同义词等问题,导致相似度计算并不准确。在前人研究的基础上,提出了一种融合主题域相似的个性化排序方法。该方法首先通过主题域的划分,将不同主题含义的网页及标签分开,通过构建的标签相似网络找出标签同义词。然后结合用户标签和主题偏好找出兴趣相近的用户,并对用户的标注信息进行扩展,从而能够有效地改善个性化信息检索的效果。在真实数据上的实验结果表明,该方法能有效缓解标注稀疏性和标签同义词问题,有助于改善用户检索体验。     

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.     

个性化的社会标签查询扩展技术研究

随着互联网上的信息日益增长,个性化的搜索需求越来越迫切,由于用户兴趣的不同和行为的差异,如何为不同的用户提供不同的检索结果成为一个具有挑战性的问题。首先对现有搜索引擎的个性化信息检索和查询扩展技术进行了分类总结,分析了它们各自的优缺点。然后提出了基于社会化标签的个性化查询词扩展方法。这些方法通过从用户所收藏的社会化标签或标签所对应的网页中提取出和用户查询词相关的词,来对用户的初始查询进行扩展。最后利用Delicious网站上的用户数据,对比研究了这几种个性化查询扩展算法。通过与Google进行对比分析实验,结果表明所提出的社会化标签的个性化查询词扩展方法能够较好地满足用户的个性化需求,检索结果比Google的检索结果更接近用户需求。     

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。     

Learning Social Tag Relevance by Neighbor Voting

Social image analysis and retrieval is important for helping people organize and access the increasing amount of user tagged multimedia. Since user tagging is known to be uncontrolled, ambiguous, and overly personalized, a fundamental problem is how to interpret the relevance of a user-contributed tag with respect to the visual content the tag is describing. Intuitively, if different persons label visually similar images using the same tags, these tags are likely to reflect objective aspects of the visual content. Starting from this intuition, we propose in this paper a neighbor voting algorithm which accurately and efficiently learns tag relevance by accumulating votes from visual neighbors. Under a set of well-defined and realistic assumptions, we prove that our algorithm is a good tag relevance measurement for both image ranking and tag ranking. Three experiments on 3.5 million Flickr photos demonstrate the general applicability of our algorithm in both social image retrieval and image tag suggestion. Our tag relevance learning algorithm substantially improves upon baselines for all the experiments. The results suggest that the proposed algorithm is promising for real-world applications.     

蚁群算法在网络教育资源检索中的应用

为了克服传统检索算法在个性化检索上的不足,提出了基于蚁群算法的资源检索模块.该模块挖掘Web日志中的用户向量,根据向量的相关度寻找当前用户的邻近用户.模拟蚁群算法建立概率模型,并按照概率值对资源进行降序排列,将结果提供给用户作为决策支持.实验表明新的检索模块优化了资源检索过程,提高了检索效率,实现了个性化网络教学资源检索.最后分析了模块的优越性和局限性,并对以后的发展方向进行了展望.     

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.     

面向查询意图的信息检索技术

用户兴趣和行为的多样性使得为不同用户提供更符合其查询意图的搜索结果成为一个具有挑战性的任务.Web 2.0下的社会标签是用户为他们感兴趣的网页等对象进行标注行为的结果,用户用标签来描述自己感兴趣的话题.这些标签不但代表着用户的兴趣,而且是对网页承载信息的最好揭示.提出了面向用户查询意图的标签推荐方法,旨在把能够体现用户真正查询意图的标签选择出来.标签作为对查询关键词的补充,不仅可以弥补用户短查询的缺陷,而且可以根据标签与网页上曾被标注过的标签间的关系,更准确地判断用户查询意图与网页内容之间的相关度,从而把更符合用户查询兴趣的结果排在靠前的位置上.实验结果表明,该方法比现有的其他方法更有效,这也说明社会标注对更准确地捕捉用户真实查询意图确实有重要作用.     

一种综合标签和时间因素的个性化推荐方法

针对现存的基于标签的社会化推荐系统在构建用户兴趣模型时存在的缺陷,提出一种综合标签及其时间信息的资源推荐(TTRR)模型。此模型考虑了用户的兴趣具有时间性的特点,即用户兴趣是随着时间而变化的、用户最近新打的标签更能反映用户近期的兴趣这一特性。为此,在借鉴协同过滤思想的基础上,通过利用标签使用频率信息和项目的标注时间来构建用户评分伪矩阵;在此基础上计算目标用户的最近邻集合;最后根据邻居用户给出推荐结果。通过在CiteULike数据集上进行实验,并与传统的基于标注的推荐方法进行比较,实验结果表明,TTRR模型能够更好地反映出用户的偏好,能够显著地提高推荐准确度。     

基于RBLDA模型和交互关系的微博标签推荐算法

随着互联网技术的发展, 个性化标签推荐系统在海量信息或资源过滤中起着重要的角色. 在新浪微博平台中, 用户可以自主的给自己添加标签来表明自己的兴趣爱好. 同时, 用户也可以通过标签来搜索与自己兴趣爱好相似的用户. 针对新浪微博中大部分用户没有添加标签或添加标签数目较少的问题, 提出了一种基于RBLDA模型和交互关系的微博标签推荐算法, 它首先利用RBLDA模型来产生用户的初始标签列表, 然后再结合用户的交互关系而形成的交互图来预测用户标签的算法. 通过在新浪微博真实数据集上的实验发现, 该方案与传统的标签推荐算法相比, 取得了良好的实验效果.     

DYSCS: A platform to build geographically and semantically enhanced social content sites

Social content sites allow ordinary internet users to upload, edit, share, and annotate Web content with freely chosen keywords called tags. However, tags are only useful to the extent that they are processable by users and machines, which is often not the case since users frequently provide ambiguous and idiosyncratic tags. Thereby, many social content sites are starting to allow users to enrich their tags with semantic metadata, such as the GeoSocial Content Sites, for example, where users can annotate their tags with geographic metadata. But geographic metadata alone only unveils a very specific facet of a tag, which leads to the need for more general purpose semantic metadata. This paper introduces DYSCS – Do it Yourself Social Content Sites – a platform that combines Web 2.0 and Semantic Web technologies for assisting users in creating their own social content sites enriched with geographic and general purpose semantics. Moreover, DYSCS is highly reusable and interoperable, which are consequences of its ontology driven architecture.     

标签推荐方法研究综述

随着互联网信息的爆炸式增长,标签(由用户指定用来描述项目的关键词)在互联网信息检索领域中变得越来越重要.为在线内容赋予合适的标签,有利于更高效的内容组织和内容消费.而标签推荐通过辅助用户进行打标签的操作,极大地提升了标签的质量,标签推荐也因此受到了研究者们的广泛关注.总结出标签推荐任务的三大特性,即项目内容的多样性、标...     

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.     

Community-Aware Resource Profiling for Personalized Search in Folksonomy

In recent years,there is a fast proliferation of collaborative tagging(a.k.a.folksonomy) systems in Web 2.0 communities.With the increasingly large amount of data,how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem.Collaborative tagging systems provide an environment for users to annotate resources,and most users give annotations according to their perspectives or feelings.However,users may have different perspectives or feelings on resources,e.g.,some of them may share similar perspectives yet have a conflict with others.Thus,modeling the profile of a resource based on tags given by all users who have annotated the resource is neither suitable nor reasonable.We propose,to tackle this problem in this paper,a community-aware approach to constructing resource profiles via social filtering.In order to discover user communities,three different strategies are devised and discussed.Moreover,we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function,to optimize personalized resources ranking based on user preferences and user issued query.We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods.The experimental results verify our observations and effectiveness of proposed method.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

社交网络用户标签预测研究

随着社交网站的流行以及用户的大规模增加,社交网络用户行为分析已经成为社交网站进行网站维护、性能优化和系统升级的重要基础,也是网络知识挖掘和信息检索的重要研究领域。为了更好地理解社交网络用户添加个人标签的行为特征,该文基于大约263万个微博用户的真实数据,对用户标签的分布进行了研究和分析。我们主要考察了用户标签的宏观分布特征,以及用户标签与关注对象的标签分布之间的联系,发现微博用户给自己添加标签时,在开始阶段倾向于使用反映个性的标签,之后会出于从众心理而选用大众化标签。我们将研究发现运用到基于关注关系的标签预测算法中,结果证实相关分析对于社交网站的标签推荐等课题具有一定的参考意义。     

Semi-automatic dynamic auxiliary-tag-aided image annotation

Image annotation is the foundation for many real-world applications. In the age of Web 2.0, image search and browsing are largely based on the tags of images. In this paper, we formulate image annotation as a multi-label learning problem, and develop a semi-automatic image annotation system. The presented system chooses proper words from a vocabulary as tags for a given image, and refines the tags with the help of the user's feedback. The refinement amounts to a novel multi-label learning framework, named Semi-Automatic Dynamic Auxiliary-Tag-Aided (SADATA), in which the classification result for one certain tag (target tag) can be boosted by the classification results of a subset of the other tags (auxiliary tags). The auxiliary tags, which have strong correlations with the target tag, are determined in terms of the normalized mutual information. We only select those tags whose correlations exceed a threshold as the auxiliary tags, so the auxiliary set is sparse. How much an auxiliary tag can contribute is dependent on the image, so we also build a probabilistic model conditioned on the auxiliary tag and the input image to adjust the weight of the auxiliary tag dynamically. For an given image, the user feedback on the tags corrects the outputs of the auxiliary classifiers and SADATA will recommend more proper tags next round. SADATA is evaluated on a large collection of Corel images. The experimental results validate the effectiveness of our dynamic auxiliary-tag-aided method. Furthermore, the performance also benefits from user feedbacks such that the annotation procedure can be significantly speeded up.