  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.
