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Product recommendation with latent review topics

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Abstract

Online customer reviews complement information from product and service providers. While the latter is directly from the source of the product and/or service, the former is generally from users of these products and/or services. Clearly, these two information sets are generated from different perspectives with possibly different sets of intentions. For a prospective customer, both these perspectives together provide a complementary set of information and support their purchase decisions. Given the different perspective and incentive structure, the information from these two source sets tends to be necessarily biased, clearly with the high probability of negative information omission from that provided by the product/service providers. Moreover, customers oftentimes face information overload during their attempts at deciphering existing online customer reviews. We attempt to alleviate this through mining hidden information in online customer reviews. We use a variant of the Latent Dirichlet Allocation (LDA) model and clustering to generate equivalent options that the customer could then use in their purchase decisions. We illustrate this using online hotel review data.

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Authors and Affiliations

  1. Operations and Information Systems, University of Massachusetts, Lowell, MA, 01854, USA

    Juheng Zhang

  2. Information Systems and Operations Management, University of Florida, Gainesville, FL, 32611, USA

    Selwyn Piramuthu

Authors
  1. Juheng Zhang
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  2. Selwyn Piramuthu
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Correspondence to Selwyn Piramuthu.

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Zhang, J., Piramuthu, S. Product recommendation with latent review topics. Inf Syst Front 20, 617–625 (2018). https://doi.org/10.1007/s10796-016-9697-z

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