Persona-Aware Tips Generation
Refereed conference paper presented and published in conference proceedings

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AbstractTips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the “persona” information which captures the intrinsic language style of the users or the different characteristics of the product items. In order to exploit the persona information, we propose a framework based on adversarial variational auto-encoders (aVAE) for persona modeling from the historical tips and reviews of users and items. The latent variables from aVAE are regarded as persona embeddings. Besides representing persona using the latent embeddings, we design a persona memory for storing the persona related words for users and items. Pointer Network is used to retrieve persona wordings from the memory when generating tips. Moreover, the persona embeddings are used as latent factors by a rating prediction component to predict the sentiment of a user over an item. Finally, the persona embeddings and the sentiment information are incorporated into a recurrent neural networks based tips generation component. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.
Acceptance Date13/05/2019
All Author(s) ListPiji Li, Zihao Wang, Lidong Bing, Wai Lam
Name of ConferenceThe World Wide Web Conference (WWW 19)
Start Date of Conference13/05/2019
End Date of Conference17/05/2019
Place of ConferenceSan Francisco
Country/Region of ConferenceUnited States of America
Proceedings TitleThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
Pages1006 - 1016
LanguagesEnglish-United Kingdom

Last updated on 2021-09-04 at 00:36