2026-11-11

Other changes reviewed today:

• X switched the default ads blender for For You back to slot-spread after one week and added shuffled-history retrieval, off by default.
• Kuaishou published a paper on RecoGEM, a serving system dedicated to generative recommendation that serves 400 million DAU per day on average.

1Interests users add are applied separately at each funnel stage — Facebook

• Each "Interested In" interest gets four dedicated retrieval sources verified — blog.
  • Two find matching content directly through search-like topic clusters, and two widen the range through a content graph and item-to-item similarity.
  • Each request fetches candidates for only one interest, so when there are several interests they take turns across requests.
  • The post says that without these sources, content for a new interest might not enter the ranking pipeline at all.
• Candidates semantically close to an interest receive a boost in late ranking verified — same blog post.
  • The relevance check is an embedding-based comparison between the meaning of the interest and the candidate.
• The control layer of re-ranking (the stage that reorders a list that already has ranking scores) bounds the volume of each interest verified — same blog post.
  • It spaces out recommendations for the same interest and caps each interest's daily contribution.
  • A new interest receives extra weight for a limited period and afterward is learned from engagement responses. The values for the period, the weight, and the cap are not stated.

"Each interest in Interested In drives four dedicated retrieval sources." "The control layer spaces out recommendations for the same interest and caps each interest’s daily contribution. Retrieval also fetches candidates for only one interest per request, so every interest gets a turn. New interests receive extra weight for a limited period." — Your Algorithm blog (blog), 2026-11-10

• When an interest is added or moved, a small LLM checks whether it holds up as a topic, finds duplicates, and maps it to a cleaned-up topic name verified — same blog post.
  • A verified interest gets an embedding and search keywords and is stored in a preference store that Feed and Reels read together.
  • Integrity and safety checks are handled by a dedicated component separate from the LLM.
• "Not Interested" is applied in layers across several stages verified — same blog post.
  • During ranking, the same embedding comparison removes candidates that are too close, and closer ones are lowered by demotion (a treatment that lowers impression rank) rules.
  • After ranking, a fine-tuned LLM reviews the final list and removes items related to an interest recently added to "Not Interested".
  • A request to reduce AI-generated or suggestive content goes to a dedicated demotion signal, not to topic matching. If a specific creator is named, that creator's content is filtered out immediately.
• The pre-filled interest list shown on a first visit and the one-sentence summary (Lately Summary) are also produced by an LLM from past Feed and Reels watch signals verified — same blog post.
• Pre-launch evaluation had a separate LLM score the output of each LLM call against expected behavior verified — same blog post.

"The ranking funnel matches interests to candidates using embeddings, which are fast but coarse. To catch what they miss, a fine-tuned LLM reviews the ranked list and removes content it judges to be related to an interest recently added to Not Interested In. A key insight was that a small model fine-tuned to return only the items to remove was faster and more reliable than a general-purpose model judging each item individually." — Your Algorithm blog (blog), 2026-11-10

• When an interest is added, the user gets new recommendations right away through a supplementary request (supplementary tail load) outside the regular request cycle verified — blog.
• When an interest is put in "Not Interested", the client receives that interest's cluster and discards matching content already cached on the device before display verified — same blog post.
• The post has no figures such as A/B results, latency, or user counts verified — same blog post.

↑ Contents

2The default ads blender went back to slot-spread after one week — X

• The default of the ads blender in home-mixer (the service that receives For You requests) is slot_spread verified — params/param.rs:976-981.
  • The last sync time in the mirror header is 2026-11-09T16:00:34Z.
  • This is the third switch in ten days: 10-31 slot_spread, 11-03 multi_risk, 11-10 slot_spread.

param!(
    AdsBlenderType,
    String,
    "rust_home_mixer_ads_blender",
    "slot_spread"
);

• The new candidate source calls Phoenix retrieval (the two-tower candidate search of the Grok-based Phoenix model) once more with a copy of the user history whose row order is randomly shuffled verified — sources/shuffled_history_phoenix_source.rs.
  • The switch EnableShuffledHistoryRetrieval is false by default — params/param.rs:188-193.
  • The shuffle runs shuffle row by row on the Arrow columnar history and then serializes it again. The resulting candidates carry the served type (a label showing which source a candidate came from) ForYouPhoenixRetrievalShuffledHistory.
  • It is turned off for topic requests, in-network-only requests, and cache-reuse requests. The cap and the exclusion of already seen posts use the same parameters as the existing PhoenixSource.
• We infer this is an experiment that collects candidates relying less on the order information in the history, to measure the effect inferred. The basis is that it feeds input with the same model and cap but a different order, and records statistics for selected and unselected candidates separately.

• The default threshold of the filter that removes low-engagement OON (out-of-network) posts for viewers right after sign-up or return became a ratio of 0.004 verified — params/param.rs:876-887.
  • Ratio mode changed from false to true, and the threshold from 0.0 to 0.004. The default metric is fav, so the threshold is like count ÷ view count.
  • The filter switch EnableNewUserMinEngagementFilter is off by default, and the target window is 1,800 seconds after account creation or return.
  • Posts with no view count or a count of 0 are kept, and in-network posts are not checked — filters/new_user_min_engagement_filter.rs:64-108.

• Two dedicated indexes for AI-generated video (detected through author self-labeling, C2PA, Grok signatures, and so on) were added to the Phoenix retrieval index with 1-, 2-, and 7-day windows, and posts in the NSFW video index are excluded verified — phoenix-rankall/src/config/mod.rs:222-227. C2PA is a content provenance standard that embeds the generating tool and edit history in a signed file. No serving request looks up these indexes.
• A Phoenix not-interested prediction term was added to the video carousel sort score with weight 0.0 verified — util/video_carousel.rs. A test pins that, at weight −25, a video with a not-interested probability of 0.02 moves down one slot.
• The carousel header icon can now be turned on and off with a switch and is off by default verified — params/param.rs:1254-1259.
• A switch that puts a pipeline trace card of Under the Hood (a transparency tool where users see the labels on their own posts and pipeline processing) at the top of For You was added, off by default verified — for_you_server.rs.
• VF (visibility-filtering, a service that decides per post whether it may be shown to this viewer) gained a mechanism to turn a single rule on or off for the experiment group only, but the only registered experiment is for testing verified — rules/experiment.rs. Experiment exposure is logged only for requests where that rule would change the verdict.
• The safety label cache deletes the key and deletes it once more after 30 seconds, and discards cache fills built from reads more than 30 seconds old verified — safety-label-source/dragonfly.rs.
• Phoenix ranking training gained ad campaign, line item, creative format, and objective ID embedding inputs, off by default (dimension 0) verified — xrex/models/recsys_model.py.
• Experiment20Fou 0.3 was added to the list of shadow traffic (a path that sends only scoring to an experiment cluster and does not use it for serving) ratios verified — params/param.rs:91-101.
• The default training data reader was changed to a Rust parquet reader, and model reload supports incremental download between peers. There is no policy change verified — xrex/configs/data_feeds.py.

↑ Contents

3A system serving generative recommendation to 400 million DAU was published — Kuaishou

• RecoGEM has been deployed for more than nine months, serves 400 million DAU per day on average across search, ads, and recommendation, and runs about ten generative models including OneRec-v2 verified — paper HTML.
  • Most authors are affiliated with Kuaishou Technology, and the paper writes "our production lines" instead of a product name.
  • The structure is a decoupled design that leaves candidate state transitions and real-time artifact updates to a runtime outside the execution engine.
• On the OneModel workload it reaches more than 1,000 QPS per instance, P99 under 35ms, and 2.88× the throughput of the baseline system, and the cluster peak is 60,000 QPS verified — same paper.

"RecoGEM has been deployed in production for over nine months, serving our applications with an average of 400 million daily active users across search, advertising, and recommendation scenarios" — RecoGEM (paper), 2026-11-09

↑ Contents

Radar

• Kuaishou RecoGEM — a serving system dedicated to generative recommendation — arXiv 2611.12296.
• We skimmed 30 arXiv cs.IR responses (submitted 2026-11-07–11-09). TS-GEAR, generative retrieval for Taobao Shangou search ads, has deployment results but is limited to nearby-delivery search ads, so we dropped it.
• The Reddit newsroom post Introducing Reddit Reviews (2026-11-10) is a feature that groups product-related conversations in search with AI and shows them as review pages. It is unrelated to feed ranking, so we did not add it to the canon.
• The new r/RedditEng post Beyond Static Credentials (workload identity and mTLS, 2026-11-09) and the Netflix Tech Blog synopsis fact-checking post (2026-11-09) are unrelated to recommendation, so we skipped them.

↑ Contents

What to check next

↑ Contents