12 citations · 16 across the 6 of their papers we have counts for
7 papers
Calibrated Recommendations with Contextual Bandits
Diego Feijer, Himan Abdollahpouri, Sanket Gupta +7
Spotify's Home page features a variety of content types, including music, podcasts, and audiobooks. However, historical data is heavily skewed toward music, making it challenging t…
Practical Bandits: An Industry Perspective
Bram van den Akker, Olivier Jeunen, Ying Li +3
The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a…
Episodes Discovery Recommendation with Multi-Source Augmentations
Ziwei Fan, Alice Wang, Zahra Nazari
Recommender systems (RS) commonly retrieve potential candidate items for users from a massive number of items by modeling user interests based on historical interactions. However,…
Sequential Recommendation via Stochastic Self-Attention
Ziwei Fan, Zhiwei Liu, Alice Wang +4
Sequential recommendation models the dynamics of a user's previous behaviors in order to forecast the next item, and has drawn a lot of attention. Transformer-based approaches, whi…
Current Challenges and Future Directions in Podcast Information Access
Rosie Jones, Hamed Zamani, Markus Schedl +11
Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and c…
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
Zahra Nazari, Christophe Charbuillet, Johan Pages +4
Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We…