TY - GEN
T1 - Service recommendation based on social balance theory and collaborative filtering
AU - Qi, Lianyong
AU - Dou, Wanchun
AU - Zhang, Xuyun
PY - 2016
Y1 - 2016
N2 - With the increasing popularity of web service technology, many users turn to look for appropriate web services to further build their complex business applications. As an effective manner for service discovery, service recommendation technique is gaining ever-increasing attention, e.g., Collaborative Filtering (i.e., CF) recommendation. Generally, the traditional CF recommendation (e.g., user-based CF, item-based CF or hybrid CF) can achieve good recommendation results. However, due to the inherent sparsity of user-service rating data, it is possible that the target user has no similar friends and the services preferred by target user own no similar services. In this exceptional situation, traditional CF recommendation approaches cannot deliver an accurate recommendation result. In view of this shortcoming, a novel Social Balance Theory (i.e., SBT)-based service recommendation approach, i.e., RecSBT is introduced in this paper, to help improve the recommendation performance. Finally, through a set of simulation experiments deployed on MovieLens-1M dataset, we further validate the feasibility of RecSBT in terms of recommendation accuracy and recall.
AB - With the increasing popularity of web service technology, many users turn to look for appropriate web services to further build their complex business applications. As an effective manner for service discovery, service recommendation technique is gaining ever-increasing attention, e.g., Collaborative Filtering (i.e., CF) recommendation. Generally, the traditional CF recommendation (e.g., user-based CF, item-based CF or hybrid CF) can achieve good recommendation results. However, due to the inherent sparsity of user-service rating data, it is possible that the target user has no similar friends and the services preferred by target user own no similar services. In this exceptional situation, traditional CF recommendation approaches cannot deliver an accurate recommendation result. In view of this shortcoming, a novel Social Balance Theory (i.e., SBT)-based service recommendation approach, i.e., RecSBT is introduced in this paper, to help improve the recommendation performance. Finally, through a set of simulation experiments deployed on MovieLens-1M dataset, we further validate the feasibility of RecSBT in terms of recommendation accuracy and recall.
KW - Collaborative filtering
KW - Enemy user
KW - Friend user
KW - Service recommendation
KW - Social balance theory
KW - Target user
UR - http://www.scopus.com/inward/record.url?scp=84989338727&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46295-0_43
DO - 10.1007/978-3-319-46295-0_43
M3 - Conference proceeding contribution
AN - SCOPUS:84989338727
SN - 9783319462943
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 637
EP - 645
BT - Service-Oriented Computing
A2 - Sheng, Quan Z.
A2 - Stroulia, Eleni
A2 - Tata, Samir
A2 - Bhiri, Sami
PB - Springer, Springer Nature
CY - Cham, Switzerland
T2 - 14th International Conference on Service-Oriented Computing, ICSOC 2016
Y2 - 10 October 2016 through 13 October 2016
ER -