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}, pages = {1188--1203}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/phan-tuan19a/phan-tuan19a.pdf}, url = {http://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 %J Proceedings of Machine Learning Research %P 1188--1203 %U http://proceedings.mlr.press %V 101 %W PMLR %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 PMLR 101:1188-1203

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