Purpose:Currently,social tagging behavior,including social tag,online review and score information,has been investigated extensively,however,there are very few works about the relationship among them.In this paper,we ...Purpose:Currently,social tagging behavior,including social tag,online review and score information,has been investigated extensively,however,there are very few works about the relationship among them.In this paper,we have investigated the problem using Douban Website as the research object.Design/methodology/approach:Firstly,we divided social tags into those with high and low frequency counts,respectively,divided books into popular and unpopular books according to books’popularity,and chose core tags in terms of distribution;Secondly,we conducted an investigation on the relationship between social tags and books scores including comprehensive analyses and assorted analyses.Findings:The more popular the books become,the higher scores they will get.Tag frequency is not related with book scores directly,and neither does the tag distribution weight.Tags in books of'fashion'category are relatively disordered,which may associate with books miscellany and readers diversity.Research limitations:Social tags are growing dramatically,strategies and researches to this respect are just experimental exploration.Open source books,data and educational resources are not consummate.Comparative studies are necessary,but the result may be affected by researches based on data analyses.In addition,this research has been conducted only on one website,namely Douban,and the tags provided by Douban Book are not complete.All these factors could influence the versatility of the results.Practical implications:There are very a few studies that have been conducted on the relationship between tags and scores,and this research could bring a certain practical significance to popular books prediction and tags’quality research.Originality/value:Less attention has been paid to Chinese books while analyzing relationship between scores and tags of user generated content.Analyses based on the Chinese books may fill in the gap of better understanding the relationship between the two objects.展开更多
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 ret...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.展开更多
Social tagging systems have attracted plenty of research endeavors recently. The dynamic models of tag generation or tag usage are one of the key subjects of inquiry. However, the existing models do not well explain t...Social tagging systems have attracted plenty of research endeavors recently. The dynamic models of tag generation or tag usage are one of the key subjects of inquiry. However, the existing models do not well explain the "staged" power-law distribution of tag usage frequencies as observed in various social tagging systems. To cope with this, a new tag-generation model is proposed in this paper, which is based on a preferential selection mechanism influenced by the combinatorial effects of system recommendation and resource multidimensionality. Furthermore, to validate the model, the simulative results under different parameter combinations are compared with the distributions of tag usage frequencies in datasets from three famous social tagging systems, namely Delicious.com, Last.fin and Flickr. For different categories of resources of the three systems, three tag usage patterns can be identified, namely the power-law distribution with two plateaus, the power-law distribution with one plateau, and the standard power-law distribution. All the three patterns can be well fitted and explained by the proposed model.展开更多
Social tagging is one of the most important characteristics of Web 2.0 services, and social tagging systems (STS) are becoming more and more popular for users to annotate, organize and share items on the Web. Moreov...Social tagging is one of the most important characteristics of Web 2.0 services, and social tagging systems (STS) are becoming more and more popular for users to annotate, organize and share items on the Web. Moreover, online social network has been incorporated into social tagging systems. As more and more users tend to interact with real friends on the Web, personalized user recommendation service provided in social tagging systems is very appealing. In this paper, we propose a personalized user recommendation method, and our method handles not only the users' interest networks, but also the social network information. We empirically show that our method outperforms a state-of-the-art method on real dataset from Last.fro dataset and Douban.展开更多
The explosive increase in the number of images on the Internet has brought with it the great challenge of how to effectively index, retrieve, and organize these resources. Assigning proper tags to the visual content i...The explosive increase in the number of images on the Internet has brought with it the great challenge of how to effectively index, retrieve, and organize these resources. Assigning proper tags to the visual content is key to the success of many applications such as image retrieval and content mining. Although recent years have witnessed many advances in image tagging, these methods have limitations when applied to high-quality and large-scale training data that are expensive to obtain. In this paper, we propose a novel semantic neighbor learning method based on user-contributed social image datasets that can be acquired from the Web's inexhaustible social image content. In contrast to existing image tagging approaches that rely on high-quality image-tag supervision, we acquire weak supervision of our neighbor learning method by progressive neighborhood retrieval from noisy and diverse user-contributed image collections. The retrieved neighbor images are not only visually alike and partially correlated but also semantically related. We offer a step-by-step and easy-to-use implementation for the proposed method. Extensive experimentation on several datasets demonstrates that the performance of the proposed method significantly outperforms others.展开更多
With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas su...With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas such as multimedia, computer vision, and pattern recognition. Valuable auxiliary resources available on social websites, such as user-provided tags, aid in the tasks of visual understanding. Therefore, sev- eral methods have been proposed for exploring the auxiliary resources for tag refinement, image retrieval, and media sum- marization. This work conducts a comprehensive survey of recent advances in visual understanding by mining social media in order to discuss their merits and limitations. We then analyze the difficulties and challenges of visual understanding followed by several possible future research directions.展开更多
In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been...In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been addressed to unify tagging information to reveal user behaviors and preferences,extract the latent semantic relations among items,make recommendations,and so on.Specifically,this article summarizes recent progress about tag-aware recommender systems,emphasizing on the contributions from three mainstream perspectives and approaches:network-based methods,tensor-based methods,and the topic-based methods.Finally,we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.展开更多
In social tagging systems such as Delicious and Flickr,users collaboratively manage tags to annotate resources.Naturally,a social tagging system can be modeled as a (user,tag,resource) hypernetwork,where there are t...In social tagging systems such as Delicious and Flickr,users collaboratively manage tags to annotate resources.Naturally,a social tagging system can be modeled as a (user,tag,resource) hypernetwork,where there are three different types of nodes,namely users,resources and tags,and each hyperedge has three end nodes,connecting a user,a resource and a tag that the user employs to annotate the resource.Then how can we automatically cluster related users,resources and tags,respectively? This is a problem of community detection in a 3-partite,3-uniform hypernetwork.More generally,given a K-partite K-uniform (hyper)network,where each (hyper)edge is a K-tuple composed of nodes of K different types,how can we automatically detect communities for nodes of different types? In this paper,by turning this problem into a problem of finding an efficient compression of the (hyper)network's structure,we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform (hyper)network into communities,and develop a fast community detection method based on optimization.Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive,parameter-free,and scalable.We compare our method with existing methods in both synthetic and real-world datasets.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.:71273126)the Foundation for Humanities and Social Science of the Chinese Ministry of Education(Grant No.:13YJA870020)
文摘Purpose:Currently,social tagging behavior,including social tag,online review and score information,has been investigated extensively,however,there are very few works about the relationship among them.In this paper,we have investigated the problem using Douban Website as the research object.Design/methodology/approach:Firstly,we divided social tags into those with high and low frequency counts,respectively,divided books into popular and unpopular books according to books’popularity,and chose core tags in terms of distribution;Secondly,we conducted an investigation on the relationship between social tags and books scores including comprehensive analyses and assorted analyses.Findings:The more popular the books become,the higher scores they will get.Tag frequency is not related with book scores directly,and neither does the tag distribution weight.Tags in books of'fashion'category are relatively disordered,which may associate with books miscellany and readers diversity.Research limitations:Social tags are growing dramatically,strategies and researches to this respect are just experimental exploration.Open source books,data and educational resources are not consummate.Comparative studies are necessary,but the result may be affected by researches based on data analyses.In addition,this research has been conducted only on one website,namely Douban,and the tags provided by Douban Book are not complete.All these factors could influence the versatility of the results.Practical implications:There are very a few studies that have been conducted on the relationship between tags and scores,and this research could bring a certain practical significance to popular books prediction and tags’quality research.Originality/value:Less attention has been paid to Chinese books while analyzing relationship between scores and tags of user generated content.Analyses based on the Chinese books may fill in the gap of better understanding the relationship between the two objects.
基金supported by the National Basic Research 973 Program of China under Grant No. 2007CB310803the National Natural Science Foundation of China under Grant Nos. 61035004,60974086the Project of the State Key Laboratory of Software Development Environment of China under Grant Nos. SKLSDE-2010ZX-16,SKLSDE-2011ZX-08
文摘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.
基金Acknowledgements This work is partly supported by National Natural Science Foundation of China under grant No. 71371040.
文摘Social tagging systems have attracted plenty of research endeavors recently. The dynamic models of tag generation or tag usage are one of the key subjects of inquiry. However, the existing models do not well explain the "staged" power-law distribution of tag usage frequencies as observed in various social tagging systems. To cope with this, a new tag-generation model is proposed in this paper, which is based on a preferential selection mechanism influenced by the combinatorial effects of system recommendation and resource multidimensionality. Furthermore, to validate the model, the simulative results under different parameter combinations are compared with the distributions of tag usage frequencies in datasets from three famous social tagging systems, namely Delicious.com, Last.fin and Flickr. For different categories of resources of the three systems, three tag usage patterns can be identified, namely the power-law distribution with two plateaus, the power-law distribution with one plateau, and the standard power-law distribution. All the three patterns can be well fitted and explained by the proposed model.
基金supported by the National Basic Research Program of China (2009CB320505)the Hi-Tech Research and Development Program of China (20011AA 01A102)+2 种基金the Nuclear High-Based Project of China (2012ZX01039004-008)the National Nature Science Foundation of China (61002011)the Electronic Information Industry Development Fund Program 'The Development and Industrialization of Key Supporting Software in Cloud Computing (Cloud Storage Service)'
文摘Social tagging is one of the most important characteristics of Web 2.0 services, and social tagging systems (STS) are becoming more and more popular for users to annotate, organize and share items on the Web. Moreover, online social network has been incorporated into social tagging systems. As more and more users tend to interact with real friends on the Web, personalized user recommendation service provided in social tagging systems is very appealing. In this paper, we propose a personalized user recommendation method, and our method handles not only the users' interest networks, but also the social network information. We empirically show that our method outperforms a state-of-the-art method on real dataset from Last.fro dataset and Douban.
基金supported in part by the National Natural Science Foundation of China(Nos.61502094 and 61402099)Natural Science Foundation of Heilongjiang Province of China(Nos.F2016002 and F2015020)
文摘The explosive increase in the number of images on the Internet has brought with it the great challenge of how to effectively index, retrieve, and organize these resources. Assigning proper tags to the visual content is key to the success of many applications such as image retrieval and content mining. Although recent years have witnessed many advances in image tagging, these methods have limitations when applied to high-quality and large-scale training data that are expensive to obtain. In this paper, we propose a novel semantic neighbor learning method based on user-contributed social image datasets that can be acquired from the Web's inexhaustible social image content. In contrast to existing image tagging approaches that rely on high-quality image-tag supervision, we acquire weak supervision of our neighbor learning method by progressive neighborhood retrieval from noisy and diverse user-contributed image collections. The retrieved neighbor images are not only visually alike and partially correlated but also semantically related. We offer a step-by-step and easy-to-use implementation for the proposed method. Extensive experimentation on several datasets demonstrates that the performance of the proposed method significantly outperforms others.
基金This work was partially supported by the National Basic Research Program of China (973 Program) (2014CB347600), the National Natural Science Foundation of China (Grant Nos. 61522203 and U1611461), the Natural Science Foundation of Jiangsu Province (Bird0140058), and the National Ten Thousand Talent Program of China (Young Top-Notch Talent).
文摘With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas such as multimedia, computer vision, and pattern recognition. Valuable auxiliary resources available on social websites, such as user-provided tags, aid in the tasks of visual understanding. Therefore, sev- eral methods have been proposed for exploring the auxiliary resources for tag refinement, image retrieval, and media sum- marization. This work conducts a comprehensive survey of recent advances in visual understanding by mining social media in order to discuss their merits and limitations. We then analyze the difficulties and challenges of visual understanding followed by several possible future research directions.
基金supported by the Future and Emerging Technologies (FET) Programs of the European Commission FP7-COSI-ICT(QLectives with Grant No.231200 and Liquid Pub with Grant No.213360)Z.-K.Zhang and T.Zhou acknowledge the National Natural Science Foundation of China under Grant Nos.11105024,60973069,61103109,and 90924011the Science and Technology Department of Sichuan Province under Grant No.2010HH0002
文摘In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been addressed to unify tagging information to reveal user behaviors and preferences,extract the latent semantic relations among items,make recommendations,and so on.Specifically,this article summarizes recent progress about tag-aware recommender systems,emphasizing on the contributions from three mainstream perspectives and approaches:network-based methods,tensor-based methods,and the topic-based methods.Finally,we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.
基金supported in part by JSPS Grant-in-Aid under Grant No.22300049 and IBM Ph.D.Fellowship
文摘In social tagging systems such as Delicious and Flickr,users collaboratively manage tags to annotate resources.Naturally,a social tagging system can be modeled as a (user,tag,resource) hypernetwork,where there are three different types of nodes,namely users,resources and tags,and each hyperedge has three end nodes,connecting a user,a resource and a tag that the user employs to annotate the resource.Then how can we automatically cluster related users,resources and tags,respectively? This is a problem of community detection in a 3-partite,3-uniform hypernetwork.More generally,given a K-partite K-uniform (hyper)network,where each (hyper)edge is a K-tuple composed of nodes of K different types,how can we automatically detect communities for nodes of different types? In this paper,by turning this problem into a problem of finding an efficient compression of the (hyper)network's structure,we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform (hyper)network into communities,and develop a fast community detection method based on optimization.Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive,parameter-free,and scalable.We compare our method with existing methods in both synthetic and real-world datasets.