Abstract
There are more and more online sites that allow users to express their sentiments by writing reviews. Recently, researchers have paid attention to review generation. They generate review text under specific contexts, such as rating, user ID or product ID. The encoder-attention-decoder based methods achieve impressive performance in this task. However, these methods do not consider user preference when generating reviews. Only considering numeric contexts such as user ID or product ID, these methods tend to generate generic and boring reviews, which results in a lack of diversity when generating reviews for different users or products. We propose a user preference-aware review generation model to take account of user preference. User preference reflects the characteristics of the user and has a great impact when the user writes reviews. Specifically, we extract keywords from users’ reviews using a score function as user preference. The decoder generates words depending on not only the context vector but also user preference when decoding. Through considering users’ preferred words explicitly, we generate diverse reviews. Experiments on a real review dataset from Amazon show that our model outperforms state-of-the-art baselines according to two evaluation metrics.
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Notes
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The data is available at https://goo.gl/TFjEH4.
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Acknowledgements
This research is supported by National Natural Science Foundation of China (Grant No. 61773229), Basic Scientific Research Program of Shenzhen City (Grant No. JCYJ20160331184440545), and Overseas Cooperation Research Fund of Graduate School at Shenzhen, Tsinghua University (Grant No. HW2018002).
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Wang, W., Zheng, HT., Liu, H. (2019). User Preference-Aware Review Generation. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_18
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