An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data |
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Authors: | Ting Bai Hong-Jian Dou Wayne Xin Zhao Ding-Yi Yang Ji-Rong Wen |
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Affiliation: | 1.School of Information,Renmin University of China,Beijing,China;2.Beijing Key Laboratory of Big Data Management and Analysis Methods,Beijing,China;3.Guangdong Key Laboratory of Big Data Analysis and Processing,Guangzhou,China |
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Abstract: | Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large. |
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