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Hybrid Recommendation in Heterogeneous Networks

  • Conference paper
User Modeling, Adaptation, and Personalization (UMAP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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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Author information

Authors and Affiliations

  1. Center for Web Intelligence, DePaul University, Chicago, IL, 60604, USA

    Robin Burke, Fatemeh Vahedian & Bamshad Mobasher

Authors
  1. Robin Burke
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  2. Fatemeh Vahedian
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  3. Bamshad Mobasher
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Editor information

Editors and Affiliations

  1. School of Computing, University of Leeds, United Kingdom

    Vania Dimitrova

  2. University of Haifa, Israel

    Tsvi Kuflik

  3. Department of Information and Computer Sciences, University of Hawaii, 96822, Honolulu, HI, USA

    David Chin

  4. Free University of Bozen - Bolzano, Piazza Domenicani 3, 39100, Bolzano, Italy

    Francesco Ricci

  5. Department of Computer Science, Aalborg University, Selma Lagerlofs Vej, 300 9220, Aalborg-East, Denmark

    Peter Dolog

  6. EEMCS - Web Information Systems, 2600 GA, Delft, The Netherlands

    Geert-Jan Houben

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© 2014 Springer International Publishing Switzerland

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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

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08785-6

  • Online ISBN: 978-3-319-08786-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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