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#23: Generative Models for Recommender Systems with Yashar Deldjoo
Manage episode 434567869 series 3288795
In episode 23 of Recsperts, we welcome Yashar Deldjoo, Assistant Professor at the Polytechnic University of Bari, Italy. Yashar's research on recommender systems includes multimodal approaches, multimedia recommender systems as well as trustworthiness and adversarial robustness, where he has published a lot of work. We discuss the evolution of generative models for recommender systems, modeling paradigms, scenarios as well as their evaluation, risks and harms.
We begin our interview with a reflection of Yashar's areas of recommender systems research so far. Starting with multimedia recsys, particularly video recommendations, Yashar covers his work around adversarial robustness and trustworthiness leading to the main topic for this episode: generative models for recommender systems. We learn about their aspects for improving beyond the (partially saturated) state of traditional recommender systems: improve effectiveness and efficiency for top-n recommendations, introduce interactivity beyond classical conversational recsys, provide personalized zero- or few-shot recommendations.
We learn about the modeling paradigms and as well about the scenarios for generative models which mainly differ by input and modelling approach: ID-based, text-based, and multimodal generative models. This is how we navigate the large field of acronyms leading us from VAEs and GANs to LLMs.
Towards the end of the episode, we also touch on the evaluation, opportunities, risks and harms of generative models for recommender systems. Yashar also provides us with an ample amount of references and upcoming events where people get the chance to know more about GenRecSys.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review
- (00:00) - Introduction
- (03:58) - About Yashar Deldjoo
- (09:34) - Motivation for RecSys
- (13:05) - Intro to Generative Models for Recommender Systems
- (44:27) - Modeling Paradigms for Generative Models
- (51:33) - Scenario 1: Interaction-Driven Recommendation
- (57:59) - Scenario 2: Text-based Recommendation
- (01:10:39) - Scenario 3: Multimodal Recommendation
- (01:24:59) - Evaluation of Impact and Harm
- (01:38:07) - Further Research Challenges
- (01:45:03) - References and Research Advice
- (01:49:39) - Closing Remarks
Links from the Episode:
- Yashar Deldjoo on LinkedIn
- Yashar's Website
- KDD 2024 Tutorial: Modern Recommender Systems Leveraging Generative AI: Fundamentals, Challenges and Opportunities
- RecSys 2024 Workshop: The 1st Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommender Systems (ROEGEN@RECSYS'24)
Papers:
- Deldjoo et al. (2024): A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
- Deldjoo et al. (2020): Recommender Systems Leveraging Multimedia Content
- Deldjoo et al. (2021): A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks
- Deldjoo et al. (2020): How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models
- Liang et al. (2018): Variational Autoencoders for Collaborative Filtering
- He et al. (2016): Visual Bayesian Personalized Ranking from Implicit Feedback
General Links:
- Follow me on LinkedIn
- Follow me on X
- Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
- Recsperts Website
29 episodes
Manage episode 434567869 series 3288795
In episode 23 of Recsperts, we welcome Yashar Deldjoo, Assistant Professor at the Polytechnic University of Bari, Italy. Yashar's research on recommender systems includes multimodal approaches, multimedia recommender systems as well as trustworthiness and adversarial robustness, where he has published a lot of work. We discuss the evolution of generative models for recommender systems, modeling paradigms, scenarios as well as their evaluation, risks and harms.
We begin our interview with a reflection of Yashar's areas of recommender systems research so far. Starting with multimedia recsys, particularly video recommendations, Yashar covers his work around adversarial robustness and trustworthiness leading to the main topic for this episode: generative models for recommender systems. We learn about their aspects for improving beyond the (partially saturated) state of traditional recommender systems: improve effectiveness and efficiency for top-n recommendations, introduce interactivity beyond classical conversational recsys, provide personalized zero- or few-shot recommendations.
We learn about the modeling paradigms and as well about the scenarios for generative models which mainly differ by input and modelling approach: ID-based, text-based, and multimodal generative models. This is how we navigate the large field of acronyms leading us from VAEs and GANs to LLMs.
Towards the end of the episode, we also touch on the evaluation, opportunities, risks and harms of generative models for recommender systems. Yashar also provides us with an ample amount of references and upcoming events where people get the chance to know more about GenRecSys.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review
- (00:00) - Introduction
- (03:58) - About Yashar Deldjoo
- (09:34) - Motivation for RecSys
- (13:05) - Intro to Generative Models for Recommender Systems
- (44:27) - Modeling Paradigms for Generative Models
- (51:33) - Scenario 1: Interaction-Driven Recommendation
- (57:59) - Scenario 2: Text-based Recommendation
- (01:10:39) - Scenario 3: Multimodal Recommendation
- (01:24:59) - Evaluation of Impact and Harm
- (01:38:07) - Further Research Challenges
- (01:45:03) - References and Research Advice
- (01:49:39) - Closing Remarks
Links from the Episode:
- Yashar Deldjoo on LinkedIn
- Yashar's Website
- KDD 2024 Tutorial: Modern Recommender Systems Leveraging Generative AI: Fundamentals, Challenges and Opportunities
- RecSys 2024 Workshop: The 1st Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommender Systems (ROEGEN@RECSYS'24)
Papers:
- Deldjoo et al. (2024): A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
- Deldjoo et al. (2020): Recommender Systems Leveraging Multimedia Content
- Deldjoo et al. (2021): A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks
- Deldjoo et al. (2020): How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models
- Liang et al. (2018): Variational Autoencoders for Collaborative Filtering
- He et al. (2016): Visual Bayesian Personalized Ranking from Implicit Feedback
General Links:
- Follow me on LinkedIn
- Follow me on X
- Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
- Recsperts Website
29 episodes
All episodes
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1 #28: Multistakeholder Recommender Systems with Robin Burke 1:35:07

1 #27: Recommender Systems at the BBC with Alessandro Piscopo and Duncan Walker 1:27:44

1 #26: Diversity in Recommender Systems with Sanne Vrijenhoek 1:35:42

1 #24: Video Recommendations at Facebook with Amey Dharwadker 1:21:20

1 #23: Generative Models for Recommender Systems with Yashar Deldjoo 1:54:58

1 #22: Pinterest Homefeed and Ads Ranking with Prabhat Agarwal and Aayush Mudgal 1:24:07

1 #21: User-Centric Evaluation and Interactive Recommender Systems with Martijn Willemsen 1:35:46

1 #20: Practical Bandits and Travel Recommendations with Bram van den Akker 1:45:06

1 #19: Popularity Bias in Recommender Systems with Himan Abdollahpouri 1:41:37

1 #18: Recommender Systems for Children and non-traditional Populations 1:39:33

1 #17: Microsoft Recommenders and LLM-based RecSys with Miguel Fierro 1:02:59

1 #16: Fairness in Recommender Systems with Michael D. Ekstrand 1:42:43

1 #15: Podcast Recommendations in the ARD Audiothek with Mirza Klimenta 1:19:11

1 #14: User Modeling and Superlinked with Daniel Svonava 1:43:13

1 #13: The Netflix Recommender System and Beyond with Justin Basilico 1:20:32

1 #12: From User Intent to Multi-Stakeholder Recommenders and Creator Economy with Rishabh Mehrotra 2:05:03

1 #11: Personalized Advertising, Economic and Generative Recommenders with Flavian Vasile 1:11:28

1 #10: Recommender Systems in Human Resources with David Graus 1:03:23

1 #9: RecPack and Modularized Personalization by Froomle with Lien Michiels and Robin Verachtert 1:27:20

1 #8: Music Recommender Systems, Fairness and Evaluation with Christine Bauer 1:10:56

1 #7: Behavioral Testing with RecList for Recommenders with Jacopo Tagliabue 1:01:56

1 #6: Purpose-Aware Privacy-Preserving Recommendations with Manel Slokom 1:39:07

1 #5: Fashion Recommendations with Zeno Gantner 1:24:06

1 #4: Adversarial Machine Learning for Recommenders with Felice Merra 1:09:17

1 #3: Bandits and Simulators for Recommenders with Olivier Jeunen 1:12:54

1 #2: Deep Learning based Recommender Systems with Even Oldridge 50:07

1 #1: Practical Recommender Systems with Kim Falk 1:19:39

1 #0: Launching Recsperts - the Recommender Systems Experts Podcast 13:29
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