--- title: From Models to Products: LLMs for Recommendation at... description: Spotify Research presents two complementary systems for LLM-based recommendation at scale, both accepted at KDD 2026. NEO is a unified, language-steerable... canonical: https://daily.dev/posts/from-models-to-products-llms-for-recommendation-at-spotify-scale-rnpxxwmdh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: From Models to Products: LLMs for Recommendation at Spotify Scale | daily.dev og:description: Spotify Research presents two complementary systems for LLM-based recommendation at scale, both accepted at KDD 2026. NEO is a unified, language-steerable... og:url: https://daily.dev/posts/from-models-to-products-llms-for-recommendation-at-spotify-scale-rnpxxwmdh og:image: https://api.daily.dev/og/posts/RNpXXwMdh.png og:image:alt: From Models to Products: LLMs for Recommendation at Spotify Scale og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # From Models to Products: LLMs for Recommendation at Spotify Scale **[Spotify Research](https://daily.dev/sources/spotify_research)** · 11 min read · 0 upvotes · 0 comments ## Summary Spotify Research presents two complementary systems for LLM-based recommendation at scale, both accepted at KDD 2026. NEO is a unified, language-steerable model that handles recommendation, search, explanation, and user understanding within a single set of weights by treating catalog items as Semantic IDs — discrete tokens derived from item content. GLIDE is a production 1B-parameter LLM deployed for podcast discovery, introducing the concept of non-habitual listening and combining short-term history with dense long-term user embeddings as soft tokens. In a 21-day A/B test covering ~20 million impressions, GLIDE increased non-habitual podcast listening by 5.4% and new-show discovery by 14.3%. Both systems rely on staged training, beam-search decoding, and hybrid evaluation combining offline metrics, human judges, and LLM-based judges to bridge the gap between retrieval accuracy and real recommendation quality. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How Netflix Taught an LLM to Recommend Movies So That You Keep Watching](https://daily.dev/posts/how-netflix-taught-an-llm-to-recommend-movies-so-that-you-keep-watching-7nsw95tq8) · ByteByteGo · 52 upvotes · 8 comments - [Teaching Large Language Models to Speak Spotify: How Semantic IDs Enable Personalization](https://daily.dev/posts/teaching-large-language-models-to-speak-spotify-how-semantic-ids-enable-personalization-i4u6pcpnq) · Spotify Research · 1 upvotes · 0 comments - [Using LLMs to build content embeddings for DoorDash search and recommendations](https://daily.dev/posts/using-llms-to-build-content-embeddings-for-doordash-search-and-recommendations-fuy4zkqul) · Doordash · 0 upvotes · 0 comments - [Our Early Journey to Transform Instacart’s Discovery Recommendations with LLMs](https://daily.dev/posts/our-early-journey-to-transform-instacart-s-discovery-recommendations-with-llms-wpv1ryxzx) · Instacart · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#spotify](https://daily.dev/tags/spotify) [View this post on daily.dev](https://daily.dev/posts/from-models-to-products-llms-for-recommendation-at-spotify-scale-rnpxxwmdh) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"From Models to Products: LLMs for Recommendation at Spotify Scale","url":"https://daily.dev/posts/from-models-to-products-llms-for-recommendation-at-spotify-scale-rnpxxwmdh","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/from-models-to-products-llms-for-recommendation-at-spotify-scale-rnpxxwmdh"},"datePublished":"2026-08-03T06:10:54.942Z","dateModified":"2026-08-03T06:11:19.346Z","description":"Spotify Research presents two complementary systems for LLM-based recommendation at scale, both accepted at KDD 2026. NEO is a unified, language-steerable...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/593b8a27677558767be3ac7e4bf85f12?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/593b8a27677558767be3ac7e4bf85f12?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Spotify Research","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Spotify Research","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/a020ee10033c40eaa05cc420c7fb45af","url":"https://daily.dev/sources/spotify_research"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/from-models-to-products-llms-for-recommendation-at-spotify-scale-rnpxxwmdh","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,llm,spotify","timeRequired":"PT11M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Spotify Research","item":"https://daily.dev/sources/spotify_research"},{"@type":"ListItem","position":3,"name":"From Models to Products: LLMs for Recommendation at Spotify Scale"}]} ```