---
id: personal-web-surfing-models/09-sources
title: 'Personal web surfing models and an ought-driven tab feed: 9. Sources'
report: https://ai.hiina.space/reports/personal-web-surfing-models.md
section: 9
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# Personal web surfing models and an ought-driven tab feed: 9. Sources

- Section 9 of 9 of the report "Personal web surfing models and an ought-driven tab feed" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/personal-web-surfing-models.md
- Previous: https://ai.hiina.space/reports/personal-web-surfing-models/08-conclusion.md

## 9. Sources

All pages below were read by the research passes, except where the text says a figure came from a secondary source. Three items are dated 2026 and were read but not independently re-verified: Intent Assistant, Compass and the Prosocial Ranking Challenge. Chang et al. (CHI 2021) figures come from CMU's write-up and ScienceAlert because the paper would not load.

- [Knowledge: public per-person link libraries](https://knowledge-web.org)
- [Gwern, Guardian Angels](https://gwern.net/guardian-angel)
- [Screencasts could be scalable data and evals for single-user emulation (LessWrong, 2026)](https://www.lesswrong.com/posts/NwrbrHmkDuKPXgLFb/screencasts-could-be-scalable-data-evals-for-single-user)
- [Gwern, virtual LessWrong comments (2024)](https://gwern.net/blog/2024-virtual-comments)
- [Fu & Pirolli, SNIF-ACT (2007)](https://apps.dtic.mil/sti/html/tr/ADA462156/index.html)
- [West & Leskovec, human wayfinding in information networks (WWW 2012)](https://archives.iw3c2.org/www2012/proceedings/proceedings/p619.pdf)
- [Gellert, web page prediction with Markov and PPM models (2021 preprint)](https://webspace.ulbsibiu.ro/arpad.gellert/html/NN_Web_Pred_v9_JWE_preprint.pdf)
- [GoWeB: next-page prediction on Edge logs](https://arxiv.org/pdf/2108.03350)
- [Google, how machine learning improved Chrome (2024)](https://blog.google/chromium/how-machine-learning-improved-chrome/)
- [Chrome Topics API](https://privacysandbox.google.com/private-advertising/topics/web)
- [blink-dev: Topics API removal thread](https://groups.google.com/a/chromium.org/g/blink-dev/c/_R85yctz4Rs/m/CkQpJil0AQAJ)
- [GRU4Rec: session-based recommendations with RNNs](https://arxiv.org/pdf/1511.06939)
- [SASRec vs BERT4Rec replicability study](https://arxiv.org/pdf/2207.07483)
- [HSTU: trillion-parameter sequential transducers (ICML 2024)](https://proceedings.mlr.press/v235/zhai24a.html)
- [TIGER: generative retrieval for recommender systems (NeurIPS 2023)](https://proceedings.neurips.cc/paper/2023/hash/20dcab0f14046a5c6b02b61da9f13229-Abstract.html)
- [TALLRec](https://arxiv.org/abs/2305.00447)
- [LLaRA](https://arxiv.org/html/2312.02445v4)
- [USER-LLM (Google Research blog)](https://research.google/blog/user-llm-efficient-llm-contextualization-with-user-embeddings/)
- [ONCE: LLM-boosted content-based recommendation](https://ar5iv.arxiv.org/html/2305.06566)
- [MIND news recommendation dataset](https://www.microsoft.com/en-us/research/?p=676704)
- [EB-NeRD news dataset](https://arxiv.org/pdf/2410.03432)
- [WebShop](https://arxiv.org/abs/2207.01206v2)
- [Mind2Web](https://arxiv.org/abs/2306.06070v1)
- [WebArena](https://arxiv.org/abs/2307.13854v4)
- [WebLINX (ICML 2024)](https://proceedings.mlr.press/v235/lu24e.html)
- [Online-Mind2Web](https://arxiv.org/abs/2504.01382v1)
- [OPeRA: real-user shopping browsing dataset (2025)](https://arxiv.org/pdf/2506.05606)
- [Lu et al., simulating real users' web shopping](https://arxiv.org/html/2503.20749v7)
- [LaMP: personalized LLM benchmark](https://arxiv.org/abs/2304.11406v4)
- [PRISM alignment dataset](https://arxiv.org/pdf/2404.16019)
- [Park et al., generative agent simulations of 1,000 people](https://arxiv.org/abs/2411.10109v1)
- [PRISM-X (2026)](https://arxiv.org/pdf/2605.13307)
- [PersonalWAB](https://arxiv.org/html/2410.17236v2)
- [Persona2Web (2026)](https://arxiv.org/html/2602.17003v1)
- [Brank et al., predicting per-user browsing trails (MSR 2004)](https://www.microsoft.com/en-us/research/wp-content/uploads/2004/11/tr-2004-122.pdf)
- [Lettkeman et al., predicting task-specific webpages for revisiting (AAAI 2006)](https://mlanthology.org/aaai/2006/lettkeman2006aaai-predicting)
- [Compass: realigning short-video feeds to reflective preferences (2026)](https://arxiv.org/abs/2608.04274)
- [SANS, Google Chrome forensics](https://www.sans.org/blog/google-chrome-forensics)
- [Chromium visit annotations database source](https://chromium.googlesource.com/chromium/src/+/HEAD/components/history/core/browser/visit_annotations_database.cc)
- [Firefox Interactions.sys.mjs source](https://arai.searchfox.org/firefox-main/source/browser/components/places/Interactions.sys.mjs)
- [Decoding Firefox session store data](https://blog.dend.ro/decoding-firefox-session-store-data/)
- [snss crate: Chrome SNSS session files](https://docs.rs/crate/snss/0.1.1)
- [chrome.tabs extension API](https://developer.chrome.com/docs/extensions/reference/api/tabs/)
- [Promnesia](https://beepb00p.xyz/exobrain/projects/promnesia.html)
- [ActivityWatch web watcher add-on](https://addons.mozilla.org/firefox/addon/aw-watcher-web/)
- [ArchiveBox](https://docs.archivebox.io/v0.4.20/README.html)
- [Screenpipe](https://screenpipe.com/about)
- [Dwell time normalization for implicit feedback](https://ar5iv.labs.arxiv.org/html/2209.09000)
- [Radlinski et al., natural-language user profiles for transparent recommendation](https://arxiv.org/abs/2205.09403v1)
- [Ramos et al., LLM-written user profiles (ACL 2024)](https://arxiv.org/abs/2402.05810v2)
- [LFM: short user profiles for pairwise preference](https://arxiv.org/pdf/2402.15623)
- [LUNAR: retrieval of past records for personalization (2026)](https://arxiv.org/html/2608.05246v2)
- [Hou et al., LLMs are zero-shot rankers for recommender systems](https://arxiv.org/abs/2305.08845v2)
- [Ji et al., data leakage in recommender evaluation](https://arxiv.org/abs/2010.11060v4)
- [Mozilla Foundation, uniqueness and re-identifiability of browsing histories](https://www.mozillafoundation.org/en/research/library/why-we-still-cant-browse-in-peace-on-the-uniqueness-and-reidentifiability-of-web-browsing-histories/)
- [IMDEA, sensitive and tracked content across a billion URLs](https://networks.imdea.org/over-150-million-websites-among-a-billion-tested-include-sensitive-and-tracked-content/)
- [Anthropic API data retention policy](https://privacy.claude.com/en/articles/7996866-how-long-does-anthropic-store-my-organizations-data)
- [Anthropic, updates to consumer terms](https://www.anthropic.com/news/updates-to-our-consumer-terms)
- [Memex (Wikipedia; Bush, "As We May Think")](https://en.wikipedia.org/wiki/Memex)
- [Knowledge README](https://raw.githubusercontent.com/raphaelsty/knowledge/main/README.md)
- [Are.na API](https://www.are.na/developers/explore)
- [Hypothesis, viewing and exporting annotations](https://web.hypothes.is/blog/viewing-and-exporting-hypothesis-annotations/)
- [Memex by WorldBrain add-on](https://addons.mozilla.org/firefox/addon/worldbrain/)
- [Histre, why](https://histre.com/blog/why/)
- [RecAgent](https://arxiv.org/abs/2306.02552v3)
- [Agent4Rec (SIGIR 2024)](https://arxiv.org/abs/2310.10108v3)
- [USimAgent](https://arxiv.org/pdf/2403.09142)
- [GUM: general user models from screen activity (2025)](https://arxiv.org/html/2505.10831v1)
- [TwinVoice (2025)](https://arxiv.org/html/2510.25536)
- [gwern.net build code: GTX.hs](https://gwern.net/static/build/GTX.hs)
- [gwern.net build code: GenerateSimilar.hs](https://gwern.net/static/build/GenerateSimilar.hs)
- [Gwern newsletter, November 2022](https://gwern.net/newsletter/2022/11)
- [gwern.net repository](https://github.com/gwern/gwern.net)
- [CMU, overcoming tab overload (on Chang et al., CHI 2021)](https://www.scs.cmu.edu/news/2021/overcoming-tab-overload)
- [ScienceAlert, tab overload survey](https://www.sciencealert.com/tab-overload-is-a-common-problem-for-people-browsing-the-internet-survey-finds)
- [Dubroy & Balakrishnan, tabbed browsing study (CHI 2010)](https://www.dgp.toronto.edu/public_user/ravin/papers/chi2010_tabbedbrowsing.pdf)
- [Tabs.do (UIST 2021)](https://par.nsf.gov/servlets/purl/10358962)
- [Arc, archived tabs help article](https://resources.arc.net/hc/en-us/articles/19228855311127)
- [BuzzFeed, when people actually read later (2013)](https://www.buzzfeednews.com/article/jwherrman/heres-when-people-actually-read-later)
- [Obendorf et al., web page revisitation revisited (CHI 2007)](https://vsis-www.informatik.uni-hamburg.de/getDoc.php/publications/280/chi2007-newformat.pdf)
- [9to5Mac, Mozilla announces Pocket shutdown](https://9to5mac.com/2025/05/22/mozilla-announces-shutdown-of-pocket/)
- [Kleinberg, Mullainathan & Raghavan, the challenge of understanding what users want](https://arxiv.org/abs/2202.11776v3)
- [Milli et al., engagement, user satisfaction and divisive content on Twitter](https://arxiv.org/html/2305.16941v6)
- [Milli et al., choosing the right weights for behavioral signals](https://arxiv.org/abs/2305.17428)
- [Jia, Bernstein et al., embedding societal values into social media feeds (CSCW 2024)](https://arxiv.org/html/2307.13912v3)
- [Prosocial Ranking Challenge results (2026)](https://arxiv.org/html/2603.19626v1)
- [BONSAI: intent-specified Bluesky feeds (2025)](https://arxiv.org/html/2509.10776v1)
- [Readwise Reader, August 2026 update](https://readwise.io/reader/update-aug2026)
- [Choi et al., Intent Assistant (CHI 2026)](https://arxiv.org/html/2510.14513v3)
- [Haliburton et al., one sec friction study (CHI 2024)](https://www.medien.ifi.lmu.de/pubdb/publications/pub/haliburton2024chi/haliburton2024chi.pdf)
- [Lukoff et al., designing for user agency on YouTube (CHI 2021)](https://arxiv.org/pdf/2101.11778)

---

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space