Helping consumers to overcome information overload with a diversified online review subset
dc.contributor.author | Jin, Zhang | |
dc.contributor.author | Zhangwen, Weng | |
dc.contributor.author | Naichen, Ni | |
dc.date.accessioned | 2019-11-22T13:49:37Z | |
dc.date.available | 2019-11-22T13:49:37Z | |
dc.date.issued | 2019-09-24 | |
dc.identifier.citation | Frontiers of Business Research in China. 2019 Sep 24;13(1):15 | |
dc.identifier.uri | https://doi.org/10.1186/s11782-019-0062-1 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/152120 | |
dc.description.abstract | Abstract Redundant online reviews often have a negative impact on the efficiency of consumers’ decision-making in their online shopping. A feasible solution for business analytics is to select a review subset from the original review corpus for consumers, which is called review selection. This study aims to address the diversified review selection problem, and proposes an effective review selection approach called Simulated Annealing-Diversified Review Selection (SA-DRS) that considers the semantic relationship of review features and the content diversity of selected reviews simultaneously. SA-DRS first constructs a feature taxonomy by utilizing the Latent Dirichlet Allocation (LDA) topic model and the Word2vec model to measure the topic relation and word context relation. Based on the established feature taxonomy, the similarity between each pair of reviews is defined and the review quality is estimated as well. Finally, diversified, high-quality reviews are selected heuristically by SA-DRS in the spirit of the simulated annealing method, forming the selected review subset. Extensive experiments are conducted on real-world e-commerce platforms to demonstrate the effectiveness of SA-DRS compared to other extant review selection approaches. | |
dc.title | Helping consumers to overcome information overload with a diversified online review subset | |
dc.type | Article | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/152120/1/11782_2019_Article_62.pdf | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s). | |
dc.date.updated | 2019-11-22T13:49:41Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.