Social influence attentive neural network for friend-enhanced recommendation

With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called FriendEnhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals a...

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Main Authors: LU, Yuanfu, XIE, Ruobing, SHI, Chuan, FANG, Yuan, WANG, Wei, ZHANG, Xu, LIN, Leyu
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5156
https://ink.library.smu.edu.sg/context/sis_research/article/6159/viewcontent/ECML20_SIAN.pdf
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spelling sg-smu-ink.sis_research-61592020-07-17T08:04:51Z Social influence attentive neural network for friend-enhanced recommendation LU, Yuanfu XIE, Ruobing SHI, Chuan FANG, Yuan WANG, Wei ZHANG, Xu LIN, Leyu With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called FriendEnhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change the recommendation paradigm, making users to pay more attention to enhanced social factors. In this paper, we first formulate the FER problem, and propose a novel Social Influence Attentive Neural network (SIAN) solution. In order to fuse rich heterogeneous information, the attentive feature aggregator in SIAN is designed to learn user and item representations at both node- and typelevels. More importantly, a social influence coupler is put forward to capture the influence of the friend referral circle in an attentive manner. Experimental results demonstrate that SIAN outperforms several stateof-the-art baselines on three real-world datasets. (Code and dataset are available at https://github.com/rootlu/SIAN. 2020-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5156 https://ink.library.smu.edu.sg/context/sis_research/article/6159/viewcontent/ECML20_SIAN.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 Heterogeneous Graph Friend-Enhanced Recommendation Social Inuence 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 Heterogeneous Graph
Friend-Enhanced Recommendation
Social Inuence
Databases and Information Systems
OS and Networks
spellingShingle Heterogeneous Graph
Friend-Enhanced Recommendation
Social Inuence
Databases and Information Systems
OS and Networks
LU, Yuanfu
XIE, Ruobing
SHI, Chuan
FANG, Yuan
WANG, Wei
ZHANG, Xu
LIN, Leyu
Social influence attentive neural network for friend-enhanced recommendation
description With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called FriendEnhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change the recommendation paradigm, making users to pay more attention to enhanced social factors. In this paper, we first formulate the FER problem, and propose a novel Social Influence Attentive Neural network (SIAN) solution. In order to fuse rich heterogeneous information, the attentive feature aggregator in SIAN is designed to learn user and item representations at both node- and typelevels. More importantly, a social influence coupler is put forward to capture the influence of the friend referral circle in an attentive manner. Experimental results demonstrate that SIAN outperforms several stateof-the-art baselines on three real-world datasets. (Code and dataset are available at https://github.com/rootlu/SIAN.
format text
author LU, Yuanfu
XIE, Ruobing
SHI, Chuan
FANG, Yuan
WANG, Wei
ZHANG, Xu
LIN, Leyu
author_facet LU, Yuanfu
XIE, Ruobing
SHI, Chuan
FANG, Yuan
WANG, Wei
ZHANG, Xu
LIN, Leyu
author_sort LU, Yuanfu
title Social influence attentive neural network for friend-enhanced recommendation
title_short Social influence attentive neural network for friend-enhanced recommendation
title_full Social influence attentive neural network for friend-enhanced recommendation
title_fullStr Social influence attentive neural network for friend-enhanced recommendation
title_full_unstemmed Social influence attentive neural network for friend-enhanced recommendation
title_sort social influence attentive neural network for friend-enhanced recommendation
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5156
https://ink.library.smu.edu.sg/context/sis_research/article/6159/viewcontent/ECML20_SIAN.pdf
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