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Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naïve graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the naïve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we propose node and edge aggregation approaches to constructing compact and effective graphs called ‘ Factor - Item bipartite graphs’ by aggregating nodes and edges of a log-metadata graph. Experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality. [ABSTRACT FROM AUTHOR]

Abstract: We present a flexible hybrid recommender system that can emulate collaborative-filtering, Content-based Filtering, context-aware recommendation, and combinations of any of these recommendation semantics. The recommendation problem is modeled as a problem of finding the most relevant nodes for a given set of query nodes on a heterogeneous graph. However, existing node ranking measures cannot fully exploit the semantics behind the different types of nodes and edges in a heterogeneous graph. To overcome the limitation, we present a novel random walk based node ranking measure, PathRank, by extending the Personalized PageRank algorithm. The proposed measure can produce node ranking results with varying semantics by discriminating the different paths on a heterogeneous graph. The experimental results show that our method can produce more diverse and effective recommendation results compared to existing approaches. [Copyright &y& Elsevier]