Product recommendation with latent review topics
| Autor: | Juheng Zhang, Selwyn Piramuthu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Service (business)
Computer Networks and Communications Computer science 02 engineering and technology Service provider Data science Latent Dirichlet allocation Information overload Theoretical Computer Science World Wide Web symbols.namesake Incentive 020204 information systems 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Product (category theory) Set (psychology) Customer to customer Software Information Systems |
| Zdroj: | Information Systems Frontiers. 20:617-625 |
| ISSN: | 1572-9419 1387-3326 |
| DOI: | 10.1007/s10796-016-9697-z |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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