From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behavior of online users


Anh Phan Tuan, Nhat Nguyen Trong, Duong Bui Trong, Linh Ngo Van, Khoat Than ;
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:1188-1203, 2019.


We demonstrate the advantages of taking into account multiple types of behavior in recommendation systems. Intuitively, each user has to do some \textbf{implicit} actions (e.g., click) before making an \textbf{explicit} decision (e.g., purchase). Previous works showed that implicit and explicit feedback has distinct properties to make a useful recommendation. However, these works exploit implicit and explicit behavior separately and therefore ignore the semantic of interaction between users and items. In this paper, we propose a novel model namely \textit{Implicit to Explicit (ITE)} which directly models the order of user actions. Furthermore, we present an extended version of ITE, namely \textit{Implicit to Explicit with Side information (ITE-Si)}, which incorporates side information to enrich the representations of users and items. The experimental results show that both ITE and ITE-Si outperform existing recommendation systems and also demonstrate the effectiveness of side information in two large scale datasets.

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