TY - GEN
T1 - Wisdom of the crowd
T2 - 2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011
AU - Shang, Shang
AU - Hui, Pan
AU - Kulkarni, Sanjeev R.
AU - Cuff, Paul W.
PY - 2011
Y1 - 2011
N2 - Recommendation systems have received considerable attention recently. However, most research has been focused on improving the performance of collaborative filtering (CF) techniques. Social networks, indispensably, provide us extra information on people's preferences, and should be considered and deployed to improve the quality of recommendations. In this paper, we propose two recommendation models, for individuals and for groups respectively, based on social contagion and social influence network theory. In the recommendation model for individuals, we improve the result of collaborative filtering prediction with social contagion outcome, which simulates the result of information cascade in the decision-making process. In the recommendation model for groups, we apply social influence network theory to take interpersonal influence into account to form a settled pattern of disagreement, and then aggregate opinions of group members. By introducing the concept of susceptibility and interpersonal influence, the settled rating results are flexible, and inclined to members whose ratings are "essential".
AB - Recommendation systems have received considerable attention recently. However, most research has been focused on improving the performance of collaborative filtering (CF) techniques. Social networks, indispensably, provide us extra information on people's preferences, and should be considered and deployed to improve the quality of recommendations. In this paper, we propose two recommendation models, for individuals and for groups respectively, based on social contagion and social influence network theory. In the recommendation model for individuals, we improve the result of collaborative filtering prediction with social contagion outcome, which simulates the result of information cascade in the decision-making process. In the recommendation model for groups, we apply social influence network theory to take interpersonal influence into account to form a settled pattern of disagreement, and then aggregate opinions of group members. By introducing the concept of susceptibility and interpersonal influence, the settled rating results are flexible, and inclined to members whose ratings are "essential".
KW - Collaborative filtering
KW - Recommendation model
KW - Social influence
UR - http://www.scopus.com/inward/record.url?scp=84856610527&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856610527&partnerID=8YFLogxK
U2 - 10.1109/ICPADS.2011.150
DO - 10.1109/ICPADS.2011.150
M3 - Conference contribution
AN - SCOPUS:84856610527
SN - 9780769545769
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 835
EP - 840
BT - Proceedings - 2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011
Y2 - 7 December 2011 through 9 December 2011
ER -