Couverture de Recsperts - Recommender Systems Experts

Recsperts - Recommender Systems Experts

De : Marcel Kurovski
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  • Résumé

  • 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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    Épisodes
    • #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 et 24 min
    • #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 et 36 min
    • #20: Practical Bandits and Travel Recommendations with Bram van den Akker

      #20: Practical Bandits and Travel Recommendations with Bram van den Akker
      Nov 16 2023

      In episode 20 of Recsperts, we welcome Bram van den Akker, Senior Machine Learning Scientist at Booking.com. Bram's work focuses on bandit algorithms and counterfactual learning. He was one of the creators of the Practical Bandits tutorial at the World Wide Web conference. We talk about the role of bandit feedback in decision making systems and in specific for recommendations in the travel industry.

      In our interview, Bram elaborates on bandit feedback and how it is used in practice. We discuss off-policy- and on-policy-bandits, and we learn that counterfactual evaluation is right for selecting the best model candidates for downstream A/B-testing, but not a replacement. We hear more about the practical challenges of bandit feedback, for example the difference between model scores and propensities, the role of stochasticity or the nitty-gritty details of reward signals. Bram also shares with us the challenges of recommendations in the travel domain, where he points out the sparsity of signals or the feedback delay.

      At the end of the episode, we can both agree on a good example for a clickbait-heavy news service in our phones.

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

      • (00:00) - Introduction
      • (02:58) - About Bram van den Akker
      • (09:16) - Motivation for Practical Bandits Tutorial
      • (16:53) - Specifics and Challenges of Travel Recommendations
      • (26:19) - Role of Bandit Feedback in Practice
      • (49:13) - Motivation for Bandit Feedback
      • (01:00:54) - Practical Start for Counterfactual Evaluation
      • (01:06:33) - Role of Business Rules
      • (01:11:26) - better cut this section coherently
      • (01:17:48) - Rewards and More
      • (01:32:45) - Closing Remarks

      Links from the Episode:
      • Bram van den Akker on LinkedIn
      • Practical Bandits: An Industry Perspective (Website)
      • Practical Bandits: An Industry Perspective (Recording)
      • Tutorial at The Web Conference 2020: Unbiased Learning to Rank: Counterfactual and Online Approaches
      • Tutorial at RecSys 2021: Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances
      • GitHub: Open Bandit Pipeline

      Papers:

      • van den Akker et al. (2023): Practical Bandits: An Industry Perspective
      • van den Akker et al. (2022): Extending Open Bandit Pipeline to Simulate Industry Challenges
      • van den Akker et al. (2019): ViTOR: Learning to Rank Webpages Based on Visual Features

      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 et 45 min
    Science Mathématiques
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