Improving Content-based and Hybrid Music Recommendation using Deep Learning

By Xinxi Wang, Ye Wang. 2014

373 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Recommender Systems, Signal Processing

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Citations in the corpus

Collaborative Deep Learning for Recommender Systems (2014, background): “On music recommendation, [21, 39] directly use conventional CNN or deep belief networks (DBN) to assist representation learning for content information, but the deep learning components of their models are deterministic without modeling the noise and hence they are less robust.”
Neural Collaborative Filtering (2017, methodology): “…deep learning models for recommendation based on implicit feedback, they primarily used DNNs for modelling auxiliary information, such as textual description of items [38], acoustic features of musics [37, 43], cross-domain behaviors of users [6], and the rich information in knowledge bases [45].”
Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, methodology): “For CTR, we follow Wang and Blei [5] for setting hyper-parameters.”
Joint Deep Modeling of Users and Items Using Reviews for Recommendation (2017, background): “In all these works [31, 36, 35], an item’s latent factors are learned from item’s content and review text is ignored.”
A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017, background): “Moreover, work (Van den Oord, Diele- man, and Schrauwen 2013; Wang and Wang 2014) directly uses convolutional neural network(CNN) or deep belief network(DBN) to obtain latent factors for content information, but they are content-based methods which only infer latent factors for items and the…”
Deep Learning Based Recommender System (2017, background): “Similarly, we can easily design an item-based RBM-CF if we clamp a given item’s rating on the visible layer.”
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks (2016, background): “There have also been nice explorations on music recommendation [10, 25] in which a CNN or deep belief network (DBN) is directly used for content-based recommendation.”
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, methodology): “For implicit feedback, neural networks have been explored to model context information, such as textual description of items [40], acoustic features of musics [41], cross-domain behaviors of users [9] and the information in knowledge bases [51].”
Neural Compatibility Modeling with Attentive Knowledge Distillation (2018, methodology): “egularization parameters (i.e., λ,C) among the values {10r |r ∈{−4,···,−1}}and [2,4,6,8], respectively. In addition, the mini-batch size, the number of hidden units and learning rate were searched in [32,64,128,256], [128,256,512,1024], and [0.01,0.05,0.1], respectively. The proposed model was fine-”
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “There are also other types of recommendation applications, such as video recommendation [11, 37, 110], music recommendation [87, 167, 186], job recommendation [139], food recommendation [131], etc.”
A General Cross-Domain Recommendation Framework via Bayesian Neural Network (2018, background): “So recently some hybrid recommendation systems adopt deep neural networks to learn latent representation from auxiliary information and have achieved excellent performance in single-domain recommendation tasks [5], [13]–[16].”
Audio Super Resolution using Neural Networks (2017, background): “…learning-based algorithms have enabled advances in speech recognition (Hinton et al., 2012), audio synthesis (van den Oord et al., 2016; Mehri et al., 2016), music recommendation systems (Coviello et al., 2012; Wang & Wang, 2014; Liang et al., 2015), and in many other areas (Acevedo et al., 2009).”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “CB filtering techniques basically rely on user/item descriptions for providing item recommendation (Wang and Wang 2014).”
A review on deep learning for recommender systems: challenges and remedies (2018, background): “While some researchers utilize deep learning to extract latent factors from audio signals (Oord et al. 2013; Wang and Wang 2014; Oramas et al. 2017), some others use deep models to make music recommendations from user ratings and usage data (Ko et al.”
Towards Bayesian Deep Learning: A Survey (2016, background): “On music recommendation, [41], [61] directly use conventional CNN or deep belief networks (DBN) to assist representation learning for content information, but the deep learning components of their models are deterministic without modeling the noise and hence they are less robust.”
Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks (2015, methodology): “[15] also use a neural network to incorporate music content into the collaborative ﬁltering model.”
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (2019, background): “Neural networks are not only naturally used for feature learning to model various features of users or items, but also explored as a core recommendation model to simulate nonlinear, complex interactions between users and items (Wang and Wang 2014; Zhang et al. 2016; He et al. 2017; Cui et al. 2017).”