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In many applications, flexibility of recommendation, which is the capability of handling multiple dimensions and various recommendation types, is very important. In this paper, we focus on the flexibility of recommendation and propose a graph-based multidimensional recommendation method. We consider the problem as an entity ranking problem on the graph(More)
In this paper, we present a novel random-walk based node ranking measure, <i>PathRank</i>, which is defined on a heterogeneous graph by extending the <i>Personalized PageRank</i> algorithm. Not only can our proposed measure exploit the semantics behind the different types of nodes and edges in a heterogeneous graph, but also it can emulate various(More)
So far, many researchers have worked on recommender systems using users' implicit feedback, since it is difficult to collect explicit item preferences in most applications. Existing researches generally use a pseudo-rating matrix by adding up the number of item consumption; however, this naive approach may not capture user preferences correctly in that many(More)
In this paper, we propose an object ranking method for search and recommendation. By selecting schema-level paths and following them in an entity-relationship graph, it can incorporate diverse semantics existing in the graph. Utilizing this kind of graph-based data models has been recognized as a reasonable way for dealing with heterogeneous data. However,(More)