Social networking is an inevitable behavior of humans living in a society. In recent years, and with the rise of online social networks, personalized recommendations that leverage the social aspect have become a very intriguing domain for researchers. In this work, we explore how influence propagation and the decay in the cascading effect of influence from influential users can be leveraged to generate social graph-based recommendations. Understanding how influence propagates within a social network is itself a challenging problem. Few researchers have considered influence propagation and even fewer have considered decay in the cascading effect of influence in a social network. In this work we model the decay in influence propagation in directed graphs, utilizing the structural properties of the social graph to measure the propagated influence beyond one-hop. We then employ this influence propagation model to form social recommendations, and present our experimental results using real-life datasets.

Social recommendations have been a very intriguing domain for researchers in the past decade. The main premise is that the social network of a user can be leveraged to enhance the rating-based recommendation process. This has been achieved in various ways, and under different assumptions about the network characteristics, structure, and availability of other information (such as trust, content, etc.) In this work, we create neighborhoods of influence leveraging only the social graph structure. These are in turn introduced in the recommendation process both as a pre-processing step and as a social regularization factor of the matrix factorization algorithm. Our experimental evaluation using real-life datasets demonstrates the effectiveness of the proposed technique.

SOCIAL RECOMMENDATION SYSTEMS by Avni Gulati In recent years, with the rise of online social networks, personalized recommendations that leverage the aspect of social connections have become a very intriguing domain for researchers. In this work, we explore how influence propagation and the decay in the cascading effect of influence from influential users can be leveraged to generate social graph-based recommendations. Understanding how influence propagates within a social network is itself a challenging problem. In this research, we model the decay in influence propagation in directed graphs, utilizing the structural properties of the social graph to measure the propagated influence beyond one-hop. This social network information from influence propagation is also combined with matrix factorization as a social regularization factor. We then employ this unified framework to form social recommendations, and present our experimental results using real-life datasets. Extensive experimental analysis demonstrate that our proposed methodology outperforms state-of-the-art techniques for generating social recommendations.