Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Abstract

Abstract-The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process. Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading occurs in several rounds and has fixed number of influentials. In our approach we consider only one round of influence, which finds applications in settings where timely influence is vital. We tackle the problem by proposing a two-phase framework that aims at identifying influentials in the first phase and form influential neighborhoods to generate recommendations to users with no prior knowledge in the second phase. The difference of the proposed framework with most social recommender systems is that we need to generate recommendations including more than one item and in the absence of explicit ratings, solely relying on the social network's graph.

With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations

“…This line of work is drawing inspiration from social correlation theories such as homophily and social influence [18,19]. Most of the existing approaches regard this as a long-term, network diffusion process, and follow a graph-theoretic approach to solve the problem of identification of influentials in a social network [2,6,13].…”

Social Recommendation Systems

“…As mentioned earlier, this research extends the NewGreedy algorithm proposed by Eirinaki et al [5]. The EWThr threshold approach is equivalent to finding activated nodes evaluated by the NewGreedy approach, in which the threshold in an undirected graph is degree-dependent for every node.…”

“…The objective is to maximize influence with the minimum number of influencers, and thus this is defined as a min-max problem. We extend previous work [5] that focused on undirected graphs with no cascades, and propose a threshold-bounded influence propagation algorithm that can be applied on directed graphs, taking into account cascading of information in the network. We then propose a two-step recommendation process, in which our proposed algorithm is employed as a pre-processing step to form social graph-based user neighborhoods to be used as input to the recommendation algorithm.…”

“…In this work, we leverage the insights and results of previous work of Eirinaki et al [5], in which the NewGreedy algorithm was introduced to identify influentials in an undirected graph. In an independent cascade model this probability is independent of neighbors.…”

“…However, since the edge-weights determined are degree-dependent here, an analogy can be formed between this methodology with the NewGreedy algorithm. In order to compare and draw conclusions easily, we replicated the setup used in the experimental evaluation of [5]. Therefore, for each of the three datasets, we generated subgraphs by randomly picking nodes as seed nodes and adding their neighbors so that the graph has more than 100 vertices but fewer than 600 vertices.…”