English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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

Play Duration based User-Entity Affinity Modeling in Spoken Dialog System