AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

29 Oct 2018Weiping SongChence ShiZhiping XiaoZhijian DuanYewen XuMing ZhangJian Tang

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and recommender systems. The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features (\textit{a.k.a.}.. (read more)

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