[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-continuous-queries-could-replace-discrete-ids-in-recommenders":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},11270,"continuous-queries-could-replace-discrete-ids-in-recommenders","Continuous Queries Could Replace Discrete IDs in Recommenders","A new study swaps discrete Semantic ID beam search for continuous query generation, letting every catalog item be scored directly.","A new arXiv paper argues the way social platforms generate recommendations has a quiet math problem: the search method itself can block correct answers from ever showing up.\n\nToday's generative recommenders, in the style of the TIGER model, decode discrete Semantic IDs using finite-width beam search. The paper shows this creates a reachability constraint: a held-out target item can be perfectly scoreable yet still never enter the returned list, because the beam search never reaches it. The researchers propose two fixes. CQG-Single generates one continuous retrieval query in a single pass. CQG-AR autoregressively builds continuous residual updates step by step. Both drop the discrete token vocabulary and code lookup entirely, so every encoded item becomes directly scoreable through dense or approximate-nearest-neighbor retrieval. Across seven runs, CQG-Single beat the baseline every time while using 55.3% as many trainable parameters, and CQG-AR matched or beat it on the ML-1M, Beauty, and Sports datasets.\n\nThis matters because generative recommenders borrowed their decoding approach from large language models without much scrutiny of what that borrowing costs. Treating beam search's blind spots as a tuning problem, rather than a structural one, may have left platforms optimizing the wrong layer. A smaller model beating a bigger one on its own turf also suggests real savings in training and serving cost, assuming the result holds outside these benchmark datasets.\n\nThere's a catch: the paper's own depth ablation found that adding more continuous refinement steps doesn't reliably improve accuracy. So this isn't a clean win, it's a trade of one unsolved tuning problem for another.","[\"generative recommendation\",\"recommender systems\",\"semantic ids\",\"machine learning\"]","2026-10-12T04:00:00.000Z","2026-10-12T05:06:23.807Z","2026-10-12T05:06:29.959Z","published",null,[],"ai",[26,27,28,29],"generative recommendation","recommender systems","semantic ids","machine learning",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.12977",0,{"sections":36},[37,40,44,49,54,59,64,69,74,79,84,89,94,99],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",6896,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",946,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",496,"2026-10-11T20:06:43.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",484,"2026-10-09T12:30:00.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Hardware","hardware",234,"2026-10-11T17:20:00.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":63},"Science","science",196,"2026-10-11T15:34:40.000Z",{"name":65,"slug":66,"count":67,"latest_published_at":68},"Consumer Tech","consumer-tech",190,"2026-10-11T21:14:40.000Z",{"name":70,"slug":71,"count":72,"latest_published_at":73},"Startups","startups",120,"2026-10-09T17:02:31.000Z",{"name":75,"slug":76,"count":77,"latest_published_at":78},"Software","software",115,"2026-10-11T07:34:45.000Z",{"name":80,"slug":81,"count":82,"latest_published_at":83},"Dev Tools","dev-tools",110,"2026-10-09T13:03:48.000Z",{"name":85,"slug":86,"count":87,"latest_published_at":88},"General","general",70,"2026-10-11T14:15:00.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"Gaming","gaming",59,"2026-10-09T11:43:43.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",34,"2026-10-08T14:00:22.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",8,"2026-10-05T09:00:00.000Z"]