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Recommending the right content in large scale multimedia streaming servicesis an important and challenging problem that has received much attention in thepast decade. A key ingredient for successful recommendations is an effectivesimilarity metric between two objects, and models that leverage the currentcontext to constrain the recommendations. This work proposes a model for randomobject generation that introduces two key novel elements: (i) a similaritymetric based on the distance between objects in a given object sequence, thatis also used to measure similarity between meta-data associated with theobjects, such as artists and genres; (ii) a hierarchical graph model withdifferent graphs each associated with a different meta-data. A biased randomwalk in each graph that are coupled and synchronized dictate the randomgeneration of objects, leveraging the current context to constrain randomness.The proposed model is fully parameterized from sequences of objects, requiringno external parameters or tuning. The model is applied to a large music datasetwith over 1 million playlists generating a hierarchy with three layers (genre,artist, track). Results indicate its superiority in generating actual fullplaylists against two baseline models.

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