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Accuracy-diversity trade-off in recommender systems via graph convolutions
2021
Information Processing & Management
Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns joint convolutional representations from a nearest neighbor and a furthest neighbor graph to establish a novel accuracy-diversity trade-off for recommender
doi:10.1016/j.ipm.2020.102459
fatcat:qsirbchbqfhrbdx3b52fgdf7nq