Abstract
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 na¨ıve graph-based recommendation method, which
operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na¨ıve
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 e↵ective 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.
