BPR-UserRec:a personalized user recommendation method in social tagging systems

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.

SOCIAL TAGGING DYNAMICS UNDER SYSTEM RECOMMENDATION AND RESOURCE MULTIDIMENSIONALITY

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.

Tag-Aware Recommender Systems:A State-of-the-Art Survey

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.

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.