# Persona-Aware Tips Generation

## TL;DR

This paper introduces a novel framework for tips generation that incorporates persona information using adversarial variational auto-encoders, persona memory, and sentiment prediction to produce personalized and style-aware tips.

## Contribution

It proposes a new approach combining aVAE, persona memory, and sentiment modeling for personalized tips generation, which is a novel integration in this task.

## Key findings

- The framework effectively models persona and sentiment for tips generation.
- Experimental results show improved personalization and relevance of generated tips.
- The approach outperforms baseline methods in quality and diversity of tips.

## Abstract

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.