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.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v101-phan-tuan19a, title = {From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behavior of online users}, author = {Phan Tuan, Anh and Nguyen Trong, Nhat and Bui Trong, Duong and Ngo Van, Linh and Than, Khoat}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, pages = {1188--1203}, year = {2019}, editor = {Lee, Wee Sun and Suzuki, Taiji}, volume = {101}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/phan-tuan19a/phan-tuan19a.pdf}, url = {https://proceedings.mlr.press/v101/phan-tuan19a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behavior of online users %A Anh Phan Tuan %A Nhat Nguyen Trong %A Duong Bui Trong %A Linh Ngo Van %A Khoat Than %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-phan-tuan19a %I PMLR %P 1188--1203 %U https://proceedings.mlr.press/v101/phan-tuan19a.html %V 101 %X 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.
APA
Phan Tuan, A., Nguyen Trong, N., Bui Trong, D., Ngo Van, L. & Than, K.. (2019). From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behavior of online users. Proceedings of The Eleventh Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 101:1188-1203 Available from https://proceedings.mlr.press/v101/phan-tuan19a.html.

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