Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models

TL;DR

This paper introduces social influence-based recommendation models for individuals and groups, enhancing collaborative filtering by incorporating social contagion and influence network theories to improve recommendation accuracy.

Contribution

It presents novel models that integrate social contagion and influence theories into recommendation systems for both individuals and groups, advancing beyond traditional collaborative filtering methods.

Findings

Improved recommendation accuracy with social contagion effects.

Effective group opinion aggregation considering interpersonal influence.

Flexible ratings based on susceptibility and influence concepts.

Abstract

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".