Recsperts - Recommender Systems Experts

De: Marcel Kurovski
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  • Resumen

  • 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.
    © 2024 Marcel Kurovski
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Episodios
  • #23: Generative Models for Recommender Systems with Yashar Deldjoo

    #23: Generative Models for Recommender Systems with Yashar Deldjoo
    Aug 16 2024

    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
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    1 h y 55 m
  • #22: Pinterest Homefeed and Ads Ranking with Prabhat Agarwal and Aayush Mudgal

    #22: Pinterest Homefeed and Ads Ranking with Prabhat Agarwal and Aayush Mudgal
    Jun 6 2024
    In episode 22 of Recsperts, we welcome Prabhat Agarwal, Senior ML Engineer, and Aayush Mudgal, Staff ML Engineer, both from Pinterest, to the show. Prabhat works on recommendations and search systems at Pinterest, leading representation learning efforts. Aayush is responsible for ads ranking and privacy-aware conversion modeling. We discuss user and content modeling, short- vs. long-term objectives, evaluation as well as multi-task learning and touch on counterfactual evaluation as well.In our interview, Prabhat guides us through the journey of continuous improvements of Pinterest's Homefeed personalization starting with techniques such as gradient boosting over two-tower models to DCN and transformers. We discuss how to capture users' short- and long-term preferences through multiple embeddings and the role of candidate generators for content diversification. Prabhat shares some details about position debiasing and the challenges to facilitate exploration.With Aayush we get the chance to dive into the specifics of ads ranking at Pinterest and he helps us to better understand how multifaceted ads can be. We learn more about the pain of having too many models and the Pinterest's efforts to consolidate the model landscape to improve infrastructural costs, maintainability, and efficiency. Aayush also shares some insights about exploration and corresponding randomization in the context of ads and how user behavior is very different between different kinds of ads.Both guests highlight the role of counterfactual evaluation and its impact for faster experimentation.Towards the end of the episode, we also touch a bit on learnings from last year's RecSys challenge.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction(03:51) - Guest Introductions(09:57) - Pinterest Introduction(21:57) - Homefeed Personalization(47:27) - Ads Ranking(01:14:58) - RecSys Challenge 2023(01:20:26) - Closing RemarksLinks from the Episode:Prabhat Agarwal on LinkedInAayush Mudgal on LinkedInRecSys Challenge 2023Pinterest Engineering BlogPinterest LabsPrabhat's Talk at GTC 2022: Evolution of web-scale engagement modeling at PinterestBlogpost: How we use AutoML, Multi-task learning and Multi-tower models for Pinterest AdsBlogpost: Pinterest Home Feed Unified Lightweight Scoring: A Two-tower ApproachBlogpost: Experiment without the wait: Speeding up the iteration cycle with Offline Replay ExperimentationBlogpost: MLEnv: Standardizing ML at Pinterest Under One ML Engine to Accelerate InnovationBlogpost: Handling Online-Offline Discrepancy in Pinterest Ads Ranking SystemPapers:Eksombatchai et al. (2018): Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-TimeYing et al. (2018): Graph Convolutional Neural Networks for Web-Scale Recommender SystemsPal et al. (2020): PinnerSage: Multi-Modal User Embedding Framework for Recommendations at PinterestPancha et al. (2022): PinnerFormer: Sequence Modeling for User Representation at PinterestZhao et al. (2019): Recommending what video to watch next: a multitask ranking systemGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website
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    1 h y 24 m
  • #21: User-Centric Evaluation and Interactive Recommender Systems with Martijn Willemsen

    #21: User-Centric Evaluation and Interactive Recommender Systems with Martijn Willemsen
    Apr 8 2024

    In episode 21 of Recsperts, we welcome Martijn Willemsen, Associate Professor at the Jheronimus Academy of Data Science and Eindhoven University of Technology. Martijn's researches on interactive recommender systems which includes aspects of decision psychology and user-centric evaluation. We discuss how users gain control over recommendations, how to support their goals and needs as well as how the user-centric evaluation framework fits into all of this.

    In our interview, Martijn outlines the reasons for providing users control over recommendations and how to holistically evaluate the satisfaction and usefulness of recommendations for users goals and needs. We discuss the psychology of decision making with respect to how well or not recommender systems support it. We also dive into music recommender systems and discuss how nudging users to explore new genres can work as well as how longitudinal studies in recommender systems research can advance insights.

    Towards the end of the episode, Martijn and I also discuss some examples and the usefulness of enabling users to provide negative explicit feedback to the system.

    Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
    Don't forget to follow the podcast and please leave a review

    • (00:00) - Introduction
    • (03:03) - About Martijn Willemsen
    • (15:14) - Waves of User-Centric Evaluation in RecSys
    • (19:35) - Behaviorism is not Enough
    • (46:21) - User-Centric Evaluation Framework
    • (01:05:38) - Genre Exploration and Longitudinal Studies in Music RecSys
    • (01:20:59) - User Control and Negative Explicit Feedback
    • (01:31:50) - Closing Remarks

    Links from the Episode:
    • Martijn Willemsen on LinkedIn
    • Martijn Willemsen's Website
    • User-centric Evaluation Framework
    • Behaviorism is not Enough (Talk at RecSys 2016)
    • Neil Hunt: Quantifying the Value of Better Recommendations (Keynote at RecSys 2014)
    • What recommender systems can learn from decision psychology about preference elicitation and behavioral change (Talk at Boise State (Idaho) and Grouplens at University of Minnesota)
    • Eric J. Johnson: The Elements of Choice
    • Rasch Model
    • Spotify Web API

    Papers:

    • Ekstrand et al. (2016): Behaviorism is not Enough: Better Recommendations Through Listening to Users
    • Knijenburg et al. (2012): Explaining the user experience of recommender systems
    • Ekstrand et al. (2014): User perception of differences in recommender algorithms
    • Liang et al. (2022): Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences
    • McNee et al. (2006): Being accurate is not enough: how accuracy metrics have hurt recommender systems

    General Links:

    • Follow me on LinkedIn
    • Follow me on X
    • Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
    • Recsperts Website
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    1 h y 36 m
Matemáticas Ciencia
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