2019/11/11 by Igor de Oliveira Nunes, Nunes, Igor de Oliveira, Gabriel Matos Cardoso Leite +3
Computer Science · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Information Retrieval (cs.IR) #Music and Audio Processing #Recommender Systems and Techniques #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1911.04273
openalex publication_date 2019/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.