2015 MiningUserConsumptionIntentionf

(Ding et al., 2015) ⇒ Xiao Ding, Ting Liu, Junwen Duan, and Jian-Yun Nie. (2015). “Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network.” In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. ISBN:0-262-51129-0

Subject Headings: User Intention Classification.

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Abstract

Social media platforms are often used by people to express their needs and desires. Such data offer great opportunities to identify users' consumption intention from user-generated contents, so that better tailored products or services can be recommended. However, there have been few efforts on mining commercial intents from social media contents. In this paper, we investigate the use of social media data to identify consumption intentions for individuals. We develop a Consumption Intention Mining Model (CIMM) based on convolutional neural network (CNN), for identifying whether the user has a consumption intention. The task is domain-dependent, and learning CNN requires a large number of annotated instances, which can be available only in some domains. Hence, we investigate the possibility of transferring the CNN mid-level sentence representation learned from one domain to another by adding an adaptation layer. To demonstrate the effectiveness of CIMM, we conduct experiments on two domains. Our results show that CIMM offers a powerful paradigm for effectively identifying users' consumption intention based on their social media data. Moreover, our results also confirm that the CNN learned in one domain can be effectively transferred to another domain. This suggests that a great potential for our model to significantly increase effectiveness of product recommendations and targeted advertising.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 MiningUserConsumptionIntentionf	Ting Liu Xiao Ding Junwen Duan Jian-Yun Nie			Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network						2015