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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Burke, R., Vahedian, F., Mobasher, B. (2014). Hybrid Recommendation in Heterogeneous Networks. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_5
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DOI: https://doi.org/10.1007/978-3-319-08786-3_5
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