A Contextual Hierarchical Graph Model for Generating Random Sequences of Objects with Application to Music Playlists

TL;DR

This paper introduces a hierarchical graph model that uses contextual similarity metrics for generating realistic music playlists, outperforming baseline models in producing coherent sequences.

Contribution

It presents a novel hierarchical graph approach with a context-aware similarity metric for sequence generation, requiring no external tuning.

Findings

Outperforms baseline models in playlist generation

Effective in capturing contextual similarities in large datasets

Fully parameterized from sequence data without external tuning

Abstract

Recommending the right content in large scale multimedia streaming services is an important and challenging problem that has received much attention in the past decade. A key ingredient for successful recommendations is an effective similarity metric between two objects, and models that leverage the current context to constrain the recommendations. This work proposes a model for random object generation that introduces two key novel elements: (i) a similarity metric based on the distance between objects in a given object sequence, that is also used to measure similarity between meta-data associated with the objects, such as artists and genres; (ii) a hierarchical graph model with different graphs each associated with a different meta-data. A biased random walk in each graph that are coupled and synchronized dictate the random generation of objects, leveraging the current context to constrain randomness. The proposed model is fully parameterized from sequences of objects, requiring no external parameters or tuning. The model is applied to a large music dataset with over 1 million playlists generating a hierarchy with three layers (genre, artist, track). Results indicate its superiority in generating actual full playlists against two baseline models.