#23: Generative Models for Recommender Systems with Yashar Deldjoo

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.
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• (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

• 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

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• Recsperts Website