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        <title>An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection</title>
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        <namePart type="given">Zhenni</namePart>
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    <abstract>Spam detection has long been a research topic in both academic and industry due to its wide applications. Previous studies are mainly focused on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. Such features are either ineffective or take long time to collect and thus are hard to be applied to cold-start spam review detection tasks. Recent advance leveraged the neural network to encode the textual and behavior features for the cold-start problem. However, the abundant attribute information are largely neglected by the existing framework. In this paper, we propose a novel deep learning architecture for incorporating entities and their inherent attributes from various domains into a unified framework. Specifically, our model not only encodes the entities of reviewer, item, and review, but also their attributes such as location, date, price ranges. Furthermore, we present a domain classifier to adapt the knowledge from one domain to the other. With the abundant attributes in existing entities and knowledge in other domains, we successfully solve the problem of data scarcity in the cold-start settings. Experimental results on two Yelp datasets prove that our proposed framework significantly outperforms the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection
%A You, Zhenni
%A Qian, Tieyun
%A Liu, Bing
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F you-etal-2018-attribute
%X Spam detection has long been a research topic in both academic and industry due to its wide applications. Previous studies are mainly focused on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. Such features are either ineffective or take long time to collect and thus are hard to be applied to cold-start spam review detection tasks. Recent advance leveraged the neural network to encode the textual and behavior features for the cold-start problem. However, the abundant attribute information are largely neglected by the existing framework. In this paper, we propose a novel deep learning architecture for incorporating entities and their inherent attributes from various domains into a unified framework. Specifically, our model not only encodes the entities of reviewer, item, and review, but also their attributes such as location, date, price ranges. Furthermore, we present a domain classifier to adapt the knowledge from one domain to the other. With the abundant attributes in existing entities and knowledge in other domains, we successfully solve the problem of data scarcity in the cold-start settings. Experimental results on two Yelp datasets prove that our proposed framework significantly outperforms the state-of-the-art methods.
%U https://aclanthology.org/C18-1160
%P 1884-1895

Markdown (Informal)

[An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection](https://aclanthology.org/C18-1160) (You et al., COLING 2018)

• An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection (You et al., COLING 2018)

ACL

• Zhenni You, Tieyun Qian, and Bing Liu. 2018. An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1884–1895, Santa Fe, New Mexico, USA. Association for Computational Linguistics.