TY - GEN
T1 - Deep social collaborative filtering
AU - Fan, Wenqi
AU - Wang, Jianping
AU - Ma, Yao
AU - Tang, Jiliang
AU - Yin, Dawei
AU - Li, Qing
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/9/10
Y1 - 2019/9/10
N2 - Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative fltering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand users' preference as suggested by the social theories such as homophily and infuence. Recently, deep neural networks have been utilized for social recommendations, which facilitate both the user-item interactions and the social network information. However, most of these models cannot take full advantage of the social network information. They only use information from direct neighbors, but distant neighbors can also provide helpful information. Meanwhile, most of these models treat neighbors' information equally without considering the specifc recommendations. However, for a specifc recommendation case, the information relevant to the specifc item would be helpful. Besides, most of these models do not explicitly capture the neighbor's opinions to items for social recommendations, while diferent opinions could afect the user diferently. In this paper, to address the aforementioned challenges, we propose DSCF, a Deep Social Collaborative Filtering framework, which can exploit the social relations with various aspects for recommender systems. Comprehensive experiments on two-real world datasets show the efectiveness of the proposed framework.
AB - Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative fltering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand users' preference as suggested by the social theories such as homophily and infuence. Recently, deep neural networks have been utilized for social recommendations, which facilitate both the user-item interactions and the social network information. However, most of these models cannot take full advantage of the social network information. They only use information from direct neighbors, but distant neighbors can also provide helpful information. Meanwhile, most of these models treat neighbors' information equally without considering the specifc recommendations. However, for a specifc recommendation case, the information relevant to the specifc item would be helpful. Besides, most of these models do not explicitly capture the neighbor's opinions to items for social recommendations, while diferent opinions could afect the user diferently. In this paper, to address the aforementioned challenges, we propose DSCF, a Deep Social Collaborative Filtering framework, which can exploit the social relations with various aspects for recommender systems. Comprehensive experiments on two-real world datasets show the efectiveness of the proposed framework.
KW - Neural Networks
KW - Random Walk
KW - Recommender Systems
KW - Recurrent Neural Network
KW - Social Network
KW - Social Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85073361507&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073361507&partnerID=8YFLogxK
U2 - 10.1145/3298689.3347011
DO - 10.1145/3298689.3347011
M3 - Conference contribution
AN - SCOPUS:85073361507
T3 - RecSys 2019 - 13th ACM Conference on Recommender Systems
SP - 305
EP - 313
BT - RecSys 2019 - 13th ACM Conference on Recommender Systems
PB - Association for Computing Machinery, Inc
T2 - 13th ACM Conference on Recommender Systems, RecSys 2019
Y2 - 16 September 2019 through 20 September 2019
ER -