Spreading Activation Approach to Tag-aware Recommenders : Modeling Similarity on Multidimensional Networks
msra(2009)
摘要
Social tagging systems present a new challenge to the researchers working on recommender systems. The presence of tags, which uncover the reasons of user interests to tagged items, opens a way to increase the quality of recommendations. Yet, there is no common agreement of how the power of tags can be harnessed for recommendation. In this paper we argue for the use of spreading activation approach for building tag-aware recommender systems and suggest a specific version of this approach adapted to the multidimensional nature of social tagging networks. We introduce the asymmetric measure of relevancy (proximity) of two nodes on a multidimensional network as a cumulative strength of (weighted) multiple connections between two nodes, which includes paths and graph-structures connecting the nodes. This metric is also applicable to measure relevancy of two sub-graphs. Spreading activation methods (SAM), which usually employ breadth first search, are an efficient way to define and compute such measure taking into account not only links constituent a path, but the properties of nodes in the path such as node’s types and outdegree. We apply this notion of relevancy to measure similarity of collaborative tagging systems users and present the results of numerical simulation showing that spreading activation methods allow us to discriminate between diverse graph-structures connecting users via resources and tags. We show that the results of simulation are stable w.r.t. the variation of parameters of spreading activation algorithm used in our experiment.
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关键词
relevancy propagation,tagging,graph- based mining,structural cohesion,spreading activation,citeulike.
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