Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks

By Dawen Liang, Minshu Zhan, D. Ellis. 2015

73 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Xing Xie
2. Qing He
3. Yi Tay
4. Hui Xiong
5. Rahul G. Krishnan
6. Shuai Zhang
7. Dawen Liang
8. Aixin Sun

In Recommender Systems

1. Dawen Liang
2. Laurent Charlin
3. Xing Xie
4. Yi Tay
5. Fuzhen Zhuang
6. Hui Xiong
7. Qing He
8. Hengshu Zhu

Citations in the corpus

• Variational Autoencoders for Collaborative Filtering (2018, background): “Besides being applied in session-based sequential recommendation (see Section 2.1), various approaches [2, 28, 47, 48] have applied neural networks to incorporate side information into collaborative filtering models to better handle the cold-start problem.”
• Automatic Tagging Using Deep Convolutional Neural Networks (2016, background): “Since these are frame-level features, their statistics such as mean and variance are computed [25], or they are clustered and vector quantised [15] to obtain clip-level features.”
• Deep Learning Based Recommender System (2017, background): “ublication date: July 2018. 1:8 • S. Zhang et al. Fig. 1. Categories of deep neural network based recommendation models. Table 1. A lookup table for reviewed publications. Categories Publications MLP [2, 13, 20, 27, 38, 47, 53, 54, 66, 92, 95, 157, 166, 185], [12, 39, 93, 112, 134, 154, 182, 183] Au”
• Audio Super Resolution using Neural Networks (2017, unknown): “…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).”
• A Survey on Knowledge Graph-Based Recommender Systems (2020, methodology): “Some commonly used item side information include item attributes [7], [8], [58], [58], like brands, categories; item multimedia information, like textual description [59], image features [60], audio signals [61], video features [62]; and item reviews [9], [10].”
• Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence (2016, background): “It is also standard to further regularize the factors with side information [1, 2, 7, 12], which comprises user, item, and/or user-item covariates (e.”