Multi-Task Learning of Graph-based Inductive Representations of Music Content


Music streaming platforms rely heavily on learning meaningful representations of tracks to surface apt recommendations to users in a number of different use cases. In this work, we consider the task of learning music track representations by leveraging three rich heterogeneous sources of information: (i) organizational information (e.g., playlist co-occurrence), (ii) content information (e.g., audio and acoustics), and (iii) music stylistics (e.g., genre). We advocate for a multi-task formulation of graph representation learning, and propose MUSIG: Multi-task Sampling and Inductive learning on Graphs. MUSIG allows us to derive generalized track representations that combine the benefits offered by (i) the inductive graph based framework, which generates embeddings by sampling and aggregating features from a node’s local neighborhood, as well as, (ii) multi-task training of aggregation functions, which ensures the learnt functions perform well on a number of important tasks. We present large scale empirical results for track recommendation for the playlist completion task, and compare different classes of representation learning approaches, including collaborative filtering, word2vec and node embeddings, as well as graph embedding approaches. Our results demonstrate that considering content information (i.e., audio and acoustic features) is useful and that multi-task supervision helps learn better representations.


April 2022 | The Web Conference (WWW)

Sequential Recommendation via Stochastic Self-Attention

Ziwei Fan, Zhiwei Liu, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, Philip S. Yu

April 2022 | The Web Conference (WWW)

Using Survival Models to Estimate Long-Term Engagement in Online Experiments

Praveen Chandar, Brian St. Thomas, Lucas Maystre, Vijay Pappu, Roberto Sanchis-Ojeda, Tiffany Wu, Ben Carterette, Mounia Lalmas, Tony Jebara

April 2022 | The Web Conference (WWW)

Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations

Zahra Nazari, Praveen Chandar, Ghazal Fazelnia, Catie Edrwards, Ben Carterette, Mounia Lalmas