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Recsperts - Recommender Systems Experts
Marcel Kurovski
30 episodes
1 month ago
Recommender Systems are the most challenging, powerful and ubiquitous area of machine learning and artificial intelligence. This podcast hosts the experts in recommender systems research and application. From understanding what users really want to driving large-scale content discovery - from delivering personalized online experiences to catering to multi-stakeholder goals. Guests from industry and academia share how they tackle these and many more challenges. With Recsperts coming from universities all around the globe or from various industries like streaming, ecommerce, news, or social media, this podcast provides depth and insights. We go far beyond your 101 on RecSys and the shallowness of another matrix factorization based rating prediction blogpost! The motto is: be relevant or become irrelevant! Expect a brand-new interview each month and follow Recsperts on your favorite podcast player.
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Technology
Science,
Mathematics
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All content for Recsperts - Recommender Systems Experts is the property of Marcel Kurovski and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
Recommender Systems are the most challenging, powerful and ubiquitous area of machine learning and artificial intelligence. This podcast hosts the experts in recommender systems research and application. From understanding what users really want to driving large-scale content discovery - from delivering personalized online experiences to catering to multi-stakeholder goals. Guests from industry and academia share how they tackle these and many more challenges. With Recsperts coming from universities all around the globe or from various industries like streaming, ecommerce, news, or social media, this podcast provides depth and insights. We go far beyond your 101 on RecSys and the shallowness of another matrix factorization based rating prediction blogpost! The motto is: be relevant or become irrelevant! Expect a brand-new interview each month and follow Recsperts on your favorite podcast player.
Show more...
Technology
Science,
Mathematics
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#15: Podcast Recommendations in the ARD Audiothek with Mirza Klimenta
Recsperts - Recommender Systems Experts
1 hour 19 minutes
2 years ago
#15: Podcast Recommendations in the ARD Audiothek with Mirza Klimenta

In episode 15 of Recsperts, we delve into podcast recommendations with senior data scientist, Mirza Klimenta. Mirza discusses his work on the ARD Audiothek, a public broadcaster of audio-on-demand content, where he is part of pub. Public Value Technologies, a subsidiary of the two regional public broadcasters BR and SWR.

We explore the use and potency of simple algorithms and ways to mitigate popularity bias in data and recommendations. We also cover collaborative filtering and various approaches for content-based podcast recommendations, drawing on Mirza's expertise in multidimensional scaling for graph drawings. Additionally, Mirza sheds light on the responsibility of a public broadcaster in providing diversified content recommendations.

Towards the end of the episode, Mirza shares personal insights on his side project of becoming a novelist. Tune in for an informative and engaging conversation.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

  • (00:00) - Episode Overview
  • (01:43) - Introduction Mirza Klimenta
  • (08:06) - About ARD Audiothek
  • (21:16) - Recommenders for the ARD Audiothek
  • (30:03) - User Engagement and Feedback Signals
  • (46:05) - Optimization beyond Accuracy
  • (51:39) - Next RecSys Steps for the Audiothek
  • (57:16) - Underserved User Groups
  • (01:04:16) - Cold-Start Mitigation
  • (01:05:06) - Diversity in Recommendations
  • (01:07:50) - Further Challenges in RecSys
  • (01:10:03) - Being a Novelist
  • (01:16:07) - Closing Remarks

Links from the Episode:
  • Mirza Klimenta on LinkedIn
  • ARD Audiothek
  • pub. Public Value Technologies
  • Implicit: Fast Collaborative Filtering for Implicit Datasets
  • Fairness in Recommender Systems: How to Reduce the Popularity Bias

Papers:

  • Steck (2019): Embarrasingly Shallow Auoencoders for Sparse Data
  • Hu et al. (2008): Collaborative Filtering for Implicit Feedback Datasets
  • Cer et al. (2018): Universal Sentence Encoder

General Links:

  • Follow me on Twitter: https://twitter.com/MarcelKurovski
  • Send me your comments, questions and suggestions to marcel@recsperts.com
  • Podcast Website: https://www.recsperts.com/
Recsperts - Recommender Systems Experts
Recommender Systems are the most challenging, powerful and ubiquitous area of machine learning and artificial intelligence. This podcast hosts the experts in recommender systems research and application. From understanding what users really want to driving large-scale content discovery - from delivering personalized online experiences to catering to multi-stakeholder goals. Guests from industry and academia share how they tackle these and many more challenges. With Recsperts coming from universities all around the globe or from various industries like streaming, ecommerce, news, or social media, this podcast provides depth and insights. We go far beyond your 101 on RecSys and the shallowness of another matrix factorization based rating prediction blogpost! The motto is: be relevant or become irrelevant! Expect a brand-new interview each month and follow Recsperts on your favorite podcast player.