TY - GEN
T1 - Recommender system improvement cases through implicit feedbacks from social network
AU - Fanany, Mohamad Ivan
AU - Khasbulloh, Ibrahim Malik
AU - Jamil, Azis Maarij
AU - Taqwim, Muhammad K.A.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/12
Y1 - 2017/12/12
N2 - Recommender systems (RS) performance largely depends on diverse types of input that characterize users' preference in the form of both explicit and implicit feedbacks. An explicit feedback is stated directly by an explicit input from users regarding their interest in some options of services or products. Such feedback, however, is not always available. On the other hand, an implicit feedback, which reflects users' opinion indirectly through user behavior is far more abundant. In this paper, we elaborate several ways to improve the RS of three real cases dataset (online travel service, online transportation, and telecommunication service provider) through implicit feedbacks. In the first case, we analyze the effect of a simple feedback from users' input during registration without using any social network analysis (SNA). In the second case, we analyze the effect of community structure extracted from its SNA as its additional attributes. In the third case, we analyze the effect of more additional feedback attributes (modularity, PageRank, eigenvector centrality, clustering coefficient, weighted in degree, weighted outdegree, weighted degree) which also obtained from the SNA of the corresponding dataset. Given the right hyperparameter settings, we observed RS improvement in term of RMSE (root mean square error) in the three cases. In this paper, three RS models: SVD, SVD++, and difference SVD are used. Besides discussing the RS performance, we also discuss the computational cost incurred from incorporating those implicit feedbacks.
AB - Recommender systems (RS) performance largely depends on diverse types of input that characterize users' preference in the form of both explicit and implicit feedbacks. An explicit feedback is stated directly by an explicit input from users regarding their interest in some options of services or products. Such feedback, however, is not always available. On the other hand, an implicit feedback, which reflects users' opinion indirectly through user behavior is far more abundant. In this paper, we elaborate several ways to improve the RS of three real cases dataset (online travel service, online transportation, and telecommunication service provider) through implicit feedbacks. In the first case, we analyze the effect of a simple feedback from users' input during registration without using any social network analysis (SNA). In the second case, we analyze the effect of community structure extracted from its SNA as its additional attributes. In the third case, we analyze the effect of more additional feedback attributes (modularity, PageRank, eigenvector centrality, clustering coefficient, weighted in degree, weighted outdegree, weighted degree) which also obtained from the SNA of the corresponding dataset. Given the right hyperparameter settings, we observed RS improvement in term of RMSE (root mean square error) in the three cases. In this paper, three RS models: SVD, SVD++, and difference SVD are used. Besides discussing the RS performance, we also discuss the computational cost incurred from incorporating those implicit feedbacks.
KW - Tensorflow
KW - gradient descent
KW - latent model
KW - neighborhood
KW - recommendation system
UR - http://www.scopus.com/inward/record.url?scp=85041450934&partnerID=8YFLogxK
U2 - 10.1109/ICTC.2017.8190999
DO - 10.1109/ICTC.2017.8190999
M3 - Conference contribution
AN - SCOPUS:85041450934
T3 - International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
SP - 340
EP - 344
BT - International Conference on Information and Communication Technology Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Y2 - 18 October 2017 through 20 October 2017
ER -