Abstract
User reviews, containing a wealth of user opinion information, play an important role for product’s online word of mouth, which have great reference value for potential customers and service/product providers. But the problem of information overload caused by the massive reviews makes users difficult to find high-quality reviews effectively. Most current methods of evaluating review quality focus on review’s content. However, the reviewer’s expertise also has a positive effect on evaluation of review’s quality. In this paper, we propose a new method to rank the reviews according to their quality. Firstly, reviewer’s quality of special topic is measured based on his/her historical review data with a topic model. Then, the coverage of attributes described in review content are integrated to measure the review’s quality based on a learning to rank model. A series of experiments are implemented on a real world dataset to verify the proposed method’s effectiveness.
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Acknowledgements
The work is supported by National Natural Science Foundation of China (Nos. 61562014, U1501252, U1711263), the Guangxi Natural Science Foundation (No. 2015GXNSFAA139303), the project of Guangxi Key Laboratory of Trusted Software, the project of Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (No. YQ17111), the general Scientific Research Project of Guangxi Provincial Department of Education (No. 2017KY0195).
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Zhang, J., Lin, Y., Huang, T., Li, Y. (2018). Evaluating Review’s Quality Based on Review Content and Reviewer’s Expertise. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_4
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DOI: https://doi.org/10.1007/978-3-319-91455-8_4
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