Abstract
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 systems. The nearest neighbor graph connects entities (users or items) based on their similarities and is responsible for improving accuracy, while the furthest neighbor graph connects entities based on their dissimilarities and is responsible for diversifying recommendations. The information between the two convolutional modules is balanced already in the training phase through a regularizer inspired by multi-kernel learning. We evaluate the joint convolutional model on three benchmark datasets with different degrees of sparsity. The proposed method can either trade accuracy to improve substantially the catalog coverage or the diversity within the list; or improve both by a lesser amount. Compared with accuracy-oriented graph convolutional approaches, the proposed model shows diversity gains up to seven times by trading as little as 1% in accuracy. Compared with alternative accuracy-diversity trade-off solutions, the joint graph convolutional model retains the highest accuracy while offering a handle to increase diversity. To our knowledge, this is the first work proposing an accuracy-diversity trade-off with graph convolutions and opens the doors to learning over graphs approaches for improving such trade-off.
Highlights
Despite accuracy is still the most dominant criterion guiding the design and evaluation of recommender systems (RecSys), numerous studies have shown that recommendation diversity –decreasing the similarity of the items in the recommended item list– significantly improves user satisfaction (Aggarwal et al, 2016; Bradley & Smyth, 2001; Kaminskas & Bridge, 2016)
The model operates on a nearest neighbor (NN) graph to improve accuracy and on a furthest neighbors (FNs) graph to improve diversity
The regulariser in (22) respects the same trend as that in (5): for α → 0, NNs are mainly used for fitting since α ‖ H s‖22 → 0; vice-versa, for α → 1 the FNs are mainly used for fitting since (1 − α) ‖ H d‖22 → 0
Summary
Despite accuracy is still the most dominant criterion guiding the design and evaluation of recommender systems (RecSys), numerous studies have shown that recommendation diversity –decreasing the similarity of the items in the recommended item list– significantly improves user satisfaction (Aggarwal et al, 2016; Bradley & Smyth, 2001; Kaminskas & Bridge, 2016). We believe that in order to obtain sufficient depth in understanding the accuracy-diversity trade-off, RecSys approaches are needed that can fully capture the abovementioned complex and irregular user-item relationships. Graph convolutions have only been used to over-fit accuracy, leaving unexplored their ability to diversify recommendations and, improve the accuracy-diversity trade-off. We explore the potential of graph convolutions to improve the accuracy-diversity trade-off for recommender systems. Our specific contribution in this paper can be summarized as follows: i) We propose a novel accuracy-diversity trade-off framework for RecSys via graph convolutions. When the joint model is composed of linear graph convolutional filters, we analyze the optimality of the design problem and provide solutions for it. Iii) We analyze the joint model in the graph-spectral domain to provide an alternative interpretation of how the proposed approach balances accuracy with diversity.
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