Combining prestige and relevance ranking for personalized recommendation
Xiao Yang, Zhaoxin Zhang · 2013
In this paper, we present an adaptive graph-based personalized recommendation method based on combining prestige and relevance ranking. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships by analyzing the representation of user's preference in the graph. With different initialization and surfing strategies, this graph-based ranking model can take different type of data into account to capture personal interests from multiple perspectives. The experiments show that this algorithm can achieve better performance than the traditional CF methods and some graph-based recommendation methods.