Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Collaborative Topic Regression with Social Regularization for Tag Recommendation

Citations in the corpus

• Collaborative Deep Learning for Recommender Systems (2014, methodology): “For each dataset, similar to [35, 36], we randomly select P items associated with each user to form the training set and use all the rest of the dataset as the test set.”
• Natural-Parameter Networks: A Class of Probabilistic Neural Networks (2016, background): “The first two datasets are from [22, 23], collected separately from CiteULike in different ways to mimic different real-world settings.”
• Relational Stacked Denoising Autoencoder for Tag Recommendation (2015, methodology): “As we can see, CTR-SR outperforms CTR by incorporating relational data into the model.”
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks (2016, methodology): “Following [20, 21], we use recall as the performance measure since the ratings are in the form of implicit feedback [9, 12].”
• Collaborative Variational Autoencoder for Recommender Systems (2017, background): “Œe second dataset, citeulike-t, is independently collected by [28] and is even larger and sparser.”
• Robust Federated Recommendation System (2020, unknown): “fm [8], ML100K [15], Citeulike-a [33], and Citeulike-t [34].”
• Towards Bayesian Deep Learning: A Survey (2016, methodology): “This model is then further extended to incorporate social information [44], [57], [58].”
• A Survey on Bayesian Deep Learning (2016, background): “This model is then further extended to incorporate social information [89, 115, 116].”
• Relational Deep Learning: A Deep Latent Variable Model for Link Prediction (2017, methodology): “The first two datasets are from (Wang, Chen, and Li 2013).”