Sequential recommendation with user memory networks

User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historica...

Full description

Saved in:
Bibliographic Details
Main Authors: CHEN, Xu, XU, Hongteng, ZHANG, Yongfeng, TANG, Jiaxi, CAO, Yixin, QIN, Zheng, ZHA, Hongyuan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/7467
https://ink.library.smu.edu.sg/context/sis_research/article/8470/viewcontent/3159652.3159668.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-8470
record_format dspace
spelling sg-smu-ink.sis_research-84702022-10-20T07:07:57Z Sequential recommendation with user memory networks CHEN, Xu XU, Hongteng ZHANG, Yongfeng TANG, Jiaxi CAO, Yixin QIN, Zheng ZHA, Hongyuan User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user»s historical records and future interests. In this paper, we aim to express, store, and manipulate users» historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users» historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users» sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users» future actions are affected by previous behaviors. 2018-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7467 info:doi/10.1145/3159652.3159668 https://ink.library.smu.edu.sg/context/sis_research/article/8470/viewcontent/3159652.3159668.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Sequential Recommendation; Memory Networks; Collaborative Filtering Databases and Information Systems OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Sequential Recommendation; Memory Networks; Collaborative Filtering
Databases and Information Systems
OS and Networks
spellingShingle Sequential Recommendation; Memory Networks; Collaborative Filtering
Databases and Information Systems
OS and Networks
CHEN, Xu
XU, Hongteng
ZHANG, Yongfeng
TANG, Jiaxi
CAO, Yixin
QIN, Zheng
ZHA, Hongyuan
Sequential recommendation with user memory networks
description User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user»s historical records and future interests. In this paper, we aim to express, store, and manipulate users» historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users» historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users» sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users» future actions are affected by previous behaviors.
format text
author CHEN, Xu
XU, Hongteng
ZHANG, Yongfeng
TANG, Jiaxi
CAO, Yixin
QIN, Zheng
ZHA, Hongyuan
author_facet CHEN, Xu
XU, Hongteng
ZHANG, Yongfeng
TANG, Jiaxi
CAO, Yixin
QIN, Zheng
ZHA, Hongyuan
author_sort CHEN, Xu
title Sequential recommendation with user memory networks
title_short Sequential recommendation with user memory networks
title_full Sequential recommendation with user memory networks
title_fullStr Sequential recommendation with user memory networks
title_full_unstemmed Sequential recommendation with user memory networks
title_sort sequential recommendation with user memory networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/7467
https://ink.library.smu.edu.sg/context/sis_research/article/8470/viewcontent/3159652.3159668.pdf
_version_ 1770576351629148160