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

替代計量分析
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其它資訊
摘要Tips, 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.
出版社接受日期13.05.2019
著者Piji Li, Zihao Wang, Lidong Bing, Wai Lam
會議名稱The World Wide Web Conference (WWW 19)
會議開始日13.05.2019
會議完結日17.05.2019
會議地點San Francisco
會議國家/地區美國
會議論文集題名The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
出版年份2019
月份5
頁次1006 - 1016
國際標準書號978-1-4503-6674-8
語言英式英語

上次更新時間 2021-12-01 於 01:14