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Hybrid Recommendation in Heterogeneous Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8538)

Abstract

The social web is characterized by a wide variety of connections between individuals and entities. A challenge for recommendation is to represent and synthesize all useful aspects of a user’s profile. Typically, researchers focus on a limited set of relations (for example, person to person ties for user recommendation or annotations in social tagging recommendation).

In this paper, we present a general approach to recommendation in heterogeneous networks that can incorporate multiple relations in a weighted hybrid. A key feature of this approach is the use of the metapath, an abstraction of a class of paths in a network in which edges of different types are traversed in a particular order. A user profile is therefore a composite of multiple metapath relations. Compared to prior work with shorter metapaths, we show that a hybrid composed of components using longer metapaths yields improvements in recommendation diversity without loss of accuracy on social tagging datasets.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Center for Web IntelligenceDePaul UniversityChicagoUSA

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