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- W3176936346 abstract "Most of the review-based prediction tasks are designed for an in-domain setting, in which a review score, sentiment, or sales in a domain is predicted by reviews in the same domain. In this paper, we address the rating prediction problem in a cross-domain setting, i.e. rating prediction of an item in a domain where no review has been given for the item yet, based on reviews provided in a different domain. For example, we predict the rating of a US product in the UK market, which has never been sold in the UK, in order to decide whether to start to sell the item in the UK. For effective cross-domain rating prediction, we propose a multitask learning model that predicts both the review rating and review contents simultaneously, and found that it was effective in two ecommerce datasets from Amazon and Yahoo. Furthermore, our experimental results revealed categories and cases in which the cross-domain rating prediction was successful." @default.
- W3176936346 created "2021-07-05" @default.
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- W3176936346 date "2020-12-01" @default.
- W3176936346 modified "2023-09-27" @default.
- W3176936346 title "Cross-Domain Rating Prediction for Market Development" @default.
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- W3176936346 doi "https://doi.org/10.1109/wiiat50758.2020.00074" @default.
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