Subject
8 entries
Personalization
Bookmarks
An Image is Worth One Word: Personalizing Text-to-Image Generation Using Textual Inversion
Tel Aviv University and NVIDIA paper introducing Textual Inversion — learning a single new text embedding token that represents a user-provided concept, enabling that concept to be composed into any text prompt. Showed that the embedding space of text-to-image models is richly structured and can be expanded with just 3-5 example images.
Metarank: ML-Powered Ranking Engine
Metarank is an open-source ML-powered ranking engine — takes user feedback signals (clicks, purchases, bookmarks) and trains a Learn-to-Rank model to personalize product listings and search results. Low-code alternative to building a custom LTR pipeline.
Multi-Armed Bandits and the Stitch Fix Experimentation Platform
Stitch Fix's blog on multi-armed bandits as an alternative to A/B testing — Thompson Sampling routes traffic toward better-performing arms dynamically, reducing wasted exposure. Strong motivation for when bandits beat traditional experimentation.
Learning to Rank for Personalised Search (Yandex Kaggle Competition)
Yanir Seroussi's Kaggle competition post-mortem on Yandex Search Personalisation — applying learning-to-rank techniques to personalized search with behavioral signals. A practical case study of LTR on real search logs.
Introduction to Personalized Search
Recombee's introduction to personalized search — connecting the recommendation systems world to search, covering how behavioral signals (clicks, purchases) can be used to personalize result ranking per user. Bridges the gap between generic LTR and user-specific personalization.
Why Wikipedia's A/B Testing Is All Wrong (And How Contextual Bandits Can Fix It)
Synference blog post critiquing Wikipedia's A/B testing approach — argues that testing average effects ignores user heterogeneity, and contextual bandits could deliver personalized treatments rather than one-size-fits-all decisions.
Why Recommendation Engines Are About to Get Much Better
Coverage of advances in recommendation engine technology in 2013 — driven by larger datasets, better collaborative filtering, and contextual signals. The moment when personalization was transitioning from a luxury to an expectation.
Meshu: Turn Your Places into Beautiful Objects
Meshu turned personal location data from Foursquare and other sources into 3D-printed jewelry using Delaunay triangulation — an early example of mass-customization through algorithmic design and 3D printing.