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


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.
著者Piji Li, Zihao Wang, Lidong Bing, Wai Lam
會議名稱The World Wide Web Conference (WWW 19)
會議地點San Francisco
會議論文集題名The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
頁次1006 - 1016

上次更新時間 2021-12-01 於 01:14