Identification of Social Influence on Social Networks and Its Use in Recommender Systems: A Systematic Review

摘要

Currently the popularization of social networks has encouraged people to have more interactions on the internet through information sharing or posting activities. Different social media are a source of information that can provide valuable insight into user feedbacks, interaction history and social relationships. With this information it is possible to discover relationships of trust between people that can influence their potential behavior when purchasing a product or service. Social networks have shown to play an important role in e-commerce for the diffusion or acquisition of products. Knowing how to mine information from social networks to discover patterns of social influence can be very useful for e-commerce platforms, or for streaming of music, tv or movies. Discovering influence patterns can make item recommendations more accurate, especially when there is no knowledge about a user's tastes. This paper presents a systematic literature review that shows the main works that use social networking data to identify the most influential set of users within a social network and how this information is used in recommender systems. The results of this work show the main techniques used to calculate social influence, as well as identify which data are the most used to determine influence and which evaluation metrics are used to validate each of the proposals. From 80 papers analyzed, 14 were classified as completely relevant regarding the research questions defined in the SLR.