TY - JOUR
T1 - Collaborative Neural Social Recommendation
AU - Wu, Le
AU - Sun, Peijie
AU - Hong, Richang
AU - Ge, Yong
AU - Wang, Meng
N1 - Funding Information:
Manuscript received May 22, 2018; revised July 30, 2018; accepted September 11, 2018. Date of publication October 30, 2018; date of current version January 12, 2021. This work was supported in part by the National Key Research and Development Program under Grant 2017YFB080330, and in part by the National Natural Science Foundation of China under Grant 61602147, Grant 61432019, Grant 61732008, Grant 61725203, and Grant 61722204. This paper was recommended by Associate Editor Q. Wang. (Corresponding author: Richang Hong.) L. Wu, P. Sun, R. Hong, and M. Wang are with the School of Computer and Information, Hefei University of Technology, Hefei 230009, China (e-mail: lewu.ustc@gmail.com; sun.hfut@gmail.com; hongrc.hfut@gmail.com; eric.mengwang@gmail.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - Collaborative filtering (CF) is one of the most popular techniques for building recommender systems. To overcome the data sparsity in CF, social recommender systems have emerged to boost recommendation performance by utilizing social correlation among users' interests. Recently, inspired by the immense success of deep learning for embedding learning, neural network-based recommender systems have shown promising recommendation performance. Nevertheless, few researchers have attempted to tackle the social recommendation problem with neural models. To this end, in this paper, we design a neural architecture that organically combines the intrinsic relationship between social network structure and user-item interaction behavior for social recommendation. Two key challenges arise in this process: first, how to incorporate the social correlation of users' interests in this neural model, and second, how to design a neural architecture to capture the unique characteristics of user-item interaction behavior for recommendation. To tackle these two challenges, we develop a model named collaborative neural social recommendation (CNSR) with two parts: 1) a social embedding part and 2) a collaborative neural recommendation (CNR) part. In CNSR, the user embedding leverages each user's social embedding learned from an unsupervised deep learning technique with social correlation regularization. The user and item embeddings are then fed into a unique neural network with a newly designed collaboration layer to model both the shallow collaborative and deep complex interaction relationships between users and items. We further propose a joint learning framework to allow the social embedding part and the CNR part to mutually enhance each other. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.
AB - Collaborative filtering (CF) is one of the most popular techniques for building recommender systems. To overcome the data sparsity in CF, social recommender systems have emerged to boost recommendation performance by utilizing social correlation among users' interests. Recently, inspired by the immense success of deep learning for embedding learning, neural network-based recommender systems have shown promising recommendation performance. Nevertheless, few researchers have attempted to tackle the social recommendation problem with neural models. To this end, in this paper, we design a neural architecture that organically combines the intrinsic relationship between social network structure and user-item interaction behavior for social recommendation. Two key challenges arise in this process: first, how to incorporate the social correlation of users' interests in this neural model, and second, how to design a neural architecture to capture the unique characteristics of user-item interaction behavior for recommendation. To tackle these two challenges, we develop a model named collaborative neural social recommendation (CNSR) with two parts: 1) a social embedding part and 2) a collaborative neural recommendation (CNR) part. In CNSR, the user embedding leverages each user's social embedding learned from an unsupervised deep learning technique with social correlation regularization. The user and item embeddings are then fed into a unique neural network with a newly designed collaboration layer to model both the shallow collaborative and deep complex interaction relationships between users and items. We further propose a joint learning framework to allow the social embedding part and the CNR part to mutually enhance each other. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.
KW - Neural recommendation
KW - social correlation
KW - social embedding
KW - social recommendation
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U2 - 10.1109/TSMC.2018.2872842
DO - 10.1109/TSMC.2018.2872842
M3 - Article
AN - SCOPUS:85055860678
VL - 51
SP - 464
EP - 476
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
SN - 2168-2216
IS - 1
M1 - 8514809
ER -