  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.

Computation and Language;Artificial Intelligence;Information Retrieval