Accuracy-diversity trade-off in recommender systems via graph convolutions

Abbassi, 2007, A recommender system based on local random walks and spectral methods, 102 Adomavicius, 2008, Overcoming accuracy-diversity tradeoff in recommender systems: A variance-based approach, vol. 8 Adomavicius, 2009, Toward more diverse recommendations: Item re-ranking methods for recommender systems Adomavicius, 2011, Improving aggregate recommendation diversity using ranking-based techniques, IEEE Transactions on Knowledge and Data Engineering, 24, 896, 10.1109/TKDE.2011.15 Aggarwal, 2016, vol. 1 Anelli, 2019, The importance of being dissimilar in recommendation, 816 Berg, R. v. d., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. arXiv:1706.02263. Boyd, 2004 Bradley, 2001, Improving recommendation diversity, 85 Bridge, 2006, Ways of computing diverse collaborative recommendations, 41 Chen, 2020, Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach, vol. 34, 27 Chen, 2019, PCT: Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving, 4395 Dehmamy, 2019, Understanding the representation power of graph neural networks in learning graph topology, 15387 Eskandanian, 2020, Using stable matching to optimize the balance between accuracy and diversity in recommendation, 71 Gama, 2020, From graph filters to graph neural networks, IEEE Signal Processing Magazine, 10.1109/MSP.2020.3016143 Gan, 2013, Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities, Decision Support Systems, 55, 811, 10.1016/j.dss.2013.03.006 Gogna, 2017, Balancing accuracy and diversity in recommendations using matrix completion framework, Knowledge-Based Systems, 125, 83, 10.1016/j.knosys.2017.03.023 Goodfellow, 2016 Hamedani, 2019, Recommending the long tail items through personalized diversification, Knowledge-Based Systems, 164, 348, 10.1016/j.knosys.2018.11.004 Harper, 2015, The movielens datasets: History and context, ACM Transactions on Interactive Intelligent Systems (TIIS), 5, 1 Herlocker, 2004, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), 22, 5, 10.1145/963770.963772 Hua, 2019, Learning combination of graph filters for graph signal modeling, IEEE Signal Processing Letters, 10.1109/LSP.2019.2954981 Huang, 2017, Collaborative filtering via graph signal processing, 1094 Huang, 2018, Rating prediction via graph signal processing, IEEE Transactions on Signal Processing, 66, 5066, 10.1109/TSP.2018.2864654 Hurley, 2011, Novelty and diversity in top-n recommendation–analysis and evaluation, ACM Transactions on Internet Technology (TOIT), 10, 1, 10.1145/1944339.1944341 Hurley, 2013, Personalised ranking with diversity, 379 Ioannidis, 2019, A recurrent graph neural network for multi-relational data, 8157 Jamali, 2010, A matrix factorization technique with trust propagation for recommendation in social networks, 135 Javari, 2015, A probabilistic model to resolve diversity–accuracy challenge of recommendation systems, Knowledge and Information Systems, 44, 609, 10.1007/s10115-014-0779-2 Kaminskas, 2016, Diversity, serendipity, novelty, and coverage: Asurvey and empirical analysis of beyond-accuracy objectives in recommender systems, ACM Transactions on Interactive Intelligent Systems (TIIS), 7, 1 Karypis, 2001, Evaluation of item-based top-n recommendation algorithms, 247 Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980. Kunaver, 2015, Using latent features to measure the diversity of recommendation lists, 1230 Kunaver, 2017, Diversity in recommender systems–a survey, Knowledge-Based Systems, 123, 154, 10.1016/j.knosys.2017.02.009 Lipovetsky, 2015, Analytical closed-form solution for binary logit regression by categorical predictors, Journal of Applied Statistics, 42, 37, 10.1080/02664763.2014.932760 Liu, 2012, Solving the accuracy-diversity dilemma via directed random walks, Physical Review E, 85, 016118, 10.1103/PhysRevE.85.016118 Ma, 2011, Recommender systems with social regularization, 287 Mateos, 2019, Connecting the dots: Identifying network structure via graph signal processing, IEEE Signal Processing Magazine, 36, 16, 10.1109/MSP.2018.2890143 Mazumder, 2010, Spectral regularization algorithms for learning large incomplete matrices, Journal of Machine Learning Research, 11, 2287 Monti, 2017, Geometric matrix completion with recurrent multi-graph neural networks, 3697 Niemann, 2013, A new collaborative filtering approach for increasing the aggregate diversity of recommender systems, 955 Nikolakopoulos, 2019, Personalized diffusions for top-n recommendation, 260 Ortega, 2018, Graph signal processing: Overview, challenges, and applications, Proceedings of the IEEE, 106, 808, 10.1109/JPROC.2018.2820126 Panniello, 2014, Comparing context-aware recommender systems in terms of accuracy and diversity, User Modeling and User-Adapted Interaction, 24, 35, 10.1007/s11257-012-9135-y Rendle, 2009, BPR: Bayesian personalized ranking from implicit feedback, 452 Said, 2013, User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm, 1399 Said, 2012, Increasing diversity through furthest neighbor-based recommendation, Proceedings of the WSDM, 12 Sandryhaila, 2013, Discrete signal processing on graphs, IEEE Transactions on Signal Processing, 61, 1644, 10.1109/TSP.2013.2238935 Sandryhaila, 2014, Discrete signal processing on graphs: Frequency analysis, IEEE Transactions on Signal Processing, 62, 3042, 10.1109/TSP.2014.2321121 Sevi, H., Rilling, G., & Borgnat, P. (2018). Harmonic analysis on directed graphs and applications: From fourier analysis to wavelets. arXiv:1811.11636. Shuman, 2013, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, 30, 83, 10.1109/MSP.2012.2235192 Smyth, 2001, Similarity vs. diversity, 347 Sun, 2019, Multi-graph convolution collaborative filtering, 1306 Valcarce, 2018, On the robustness and discriminative power of information retrieval metrics for top-n recommendation, 260 Wang, 2019, Knowledge-aware graph neural networks with label smoothness regularization for recommender systems, 968 Wang, 2013, Combining user-based and item-based collaborative filtering techniques to improve recommendation diversity, 661 Wang, 2019, Neural graph collaborative filtering, 165 Wasilewski, 2016, Incorporating diversity in a learning to rank recommender system Wu, Q., Liu, Y., Miao, C., Zhao, Y., Guan, L., & Tang, H. (2019). Recent advances in diversified recommendation. arXiv:1905.06589. Wu, 2020, A comprehensive survey on graph neural networks, IEEE Transactions on Neural Networks and Learning Systems Yang, 2019, AliGraph: A comprehensive graph neural network platform, 3165 Ying, 2018, Graph convolutional neural networks for web-scale recommender systems, 974 Zeng, 2010, Can dissimilar users contribute to accuracy and diversity of personalized recommendation?, International Journal of Modern Physics C, 21, 1217, 10.1142/S0129183110015786 Zhang, 2008, Avoiding monotony: improving the diversity of recommendation lists, 123 Zhong, 2020, Hybrid graph convolutional networks with multi-head attention for location recommendation, World Wide Web, 1 Zhou, 2010, Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences, 107, 4511, 10.1073/pnas.1000488107 Ziegler, 2005, Improving recommendation lists through topic diversification, 22