  Multimedia streaming services over spoken dialog systems have become
ubiquitous. User-entity affinity modeling is critical for the system to
understand and disambiguate user intents and personalize user experiences.
However, fully voice-based interaction demands quantification of novel
behavioral cues to determine user affinities. In this work, we propose using
play duration cues to learn a matrix factorization based collaborative
filtering model. We first binarize play durations to obtain implicit positive
and negative affinity labels. The Bayesian Personalized Ranking objective and
learning algorithm are employed in our low-rank matrix factorization approach.
To cope with uncertainties in the implicit affinity labels, we propose to apply
a weighting function that emphasizes the importance of high confidence samples.
Based on a large-scale database of Alexa music service records, we evaluate the
affinity models by computing Spearman correlation between play durations and
predicted affinities. Comparing different data utilizations and weighting
functions, we find that employing both positive and negative affinity samples
with a convex weighting function yields the best performance. Further analysis
demonstrates the model's effectiveness on individual entity level and provides
insights on the temporal dynamics of observed affinities.
