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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)
Nowadays, recommender systems are widely used in various domains to help customers access to more satisfying products or services. It is expected that exploiting customers’ contextual information can improve the quality of recommendation results. Most earlier researchers assume that they already have customers’ explicit ratings on items and each rating has(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)