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
The rapid evolution of the Internet has been appealing for effective recommender systems to pinpoint useful information from online resources. Although historical rating data has been widely used as the most important...The rapid evolution of the Internet has been appealing for effective recommender systems to pinpoint useful information from online resources. Although historical rating data has been widely used as the most important information in recommendation methods, recent advancements have been demonstrating the improvement in recommendation performance with the incorporation of tag information. Furthermore, the availability of tag annotations has been well addressed by such fruitful online social tagging applications as CiteULike, MovieLens and BibSonomy, which allow users to express their preferences, upload resources and assign their own tags. Nevertheless, most existing tag-aware recommendation approaches model relationships among users, objects and tags using a tripartite graph, and hence overlook relationships within the same types of nodes. To overcome this limitation, we propose a novel approach, Trinity, to integrate historical data and tag information towards personalised recommendation. Trinity constructs a three-layered object-user-tag network that considers not only interconnections between different types of nodes but also relationships within the same types of nodes. Based on this heterogeneous network, Trinity adopts a random walk with restart model to assign the strength of associations to candidate objects, thereby providing a means of prioritizing the objects for a query user. We validate our approach via a series of large-scale 10-fold cross-validation experiments and evaluate its performance using three comprehensive criteria. Results show that our method outperforms several existing methods, including supervised random walk with restart, simulation of resource allocating processes, and traditional collaborative filtering.展开更多
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This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 71101010 and 71471016.
文摘The rapid evolution of the Internet has been appealing for effective recommender systems to pinpoint useful information from online resources. Although historical rating data has been widely used as the most important information in recommendation methods, recent advancements have been demonstrating the improvement in recommendation performance with the incorporation of tag information. Furthermore, the availability of tag annotations has been well addressed by such fruitful online social tagging applications as CiteULike, MovieLens and BibSonomy, which allow users to express their preferences, upload resources and assign their own tags. Nevertheless, most existing tag-aware recommendation approaches model relationships among users, objects and tags using a tripartite graph, and hence overlook relationships within the same types of nodes. To overcome this limitation, we propose a novel approach, Trinity, to integrate historical data and tag information towards personalised recommendation. Trinity constructs a three-layered object-user-tag network that considers not only interconnections between different types of nodes but also relationships within the same types of nodes. Based on this heterogeneous network, Trinity adopts a random walk with restart model to assign the strength of associations to candidate objects, thereby providing a means of prioritizing the objects for a query user. We validate our approach via a series of large-scale 10-fold cross-validation experiments and evaluate its performance using three comprehensive criteria. Results show that our method outperforms several existing methods, including supervised random walk with restart, simulation of resource allocating processes, and traditional collaborative filtering.
