Ranking on heterogeneous manifolds for tag recommendation in social tagging services

Nowadays, most social Websites allow users to annotate resources (such as Web pages and images) with keywords, i.e. tags. Collaborative tagging data reflects the semantic perception of users, thus providing valuable information for the related recommendation problems, e.g. tag recommendation, resource recommendation. In this paper, we tackle the problem of personalized tag recommendation in social tagging services by generalizing the traditional manifold ranking idea. Specifically, we model the complex relationships in tagging data as a heterogeneous graph and propose a novel ranking algorithmic framework for heterogeneous manifolds, named GRoMO (Graph-based Ranking of Multi-type interrelated Objects). In our system both the resource to be tagged (accounting for relevance) and the user׳s historical tags (accounting for personalization) are treated as query inputs. Then tags are ranked according to the output of GRoMO and the top tags are recommended to that user. We also explore adapting GRoMO for resource recommendation. For experiments we crawled a tagging dataset from the well-known tagging service, Del.icio.us. Experimental results indicate (1) the proposed method is effective and significantly outperforms baseline methods; (2) the iterative form solutions of GRoMO converge very fast and can be used when the dataset is large; (3) GRoMO can also be used for resource recommendation.