--- id: personal-web-surfing-models/03-existing-web-surfing-models title: 'Personal web surfing models and an ought-driven tab feed: 3. Existing web surfing models' report: https://ai.hiina.space/reports/personal-web-surfing-models.md section: 3 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: 3. Existing web surfing models - Section 3 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/02-gwern-s-guardian-angels-and-how-browsing.md - Next: https://ai.hiina.space/reports/personal-web-surfing-models/04-feasibility-of-a-personal-surfing-model-from.md ## 3. Existing web surfing models No one has published a model trained on one person's open-web history and used to rank links or tabs for them or an agent. Several neighbouring fields are mature: next-page prediction, session-based recommendation, web-agent benchmarks and personal LLMs. Each covers part of what a personal surfing model needs. | Field | Representative work | What it gives | Why it isn't a personal surfing model | |---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------| | Cognitive models of link choice | [SNIF-ACT](https://apps.dtic.mil/sti/html/tr/ADA462156/index.html) (Fu & Pirolli 2007): picks links by "information scent" relative to a goal. [West & Leskovec](https://archives.iw3c2.org/www2012/proceedings/proceedings/p619.pdf) (WWW 2012): Wikispeedia players jump to a hub, then steer by similarity | An interpretable account of why a goal-directed person clicks a link. Usable as features or a baseline | Population-level and goal-given; no personal taste | | Next-page prediction | Markov and PPM models ([Gellert 2021](https://webspace.ulbsibiu.ro/arpad.gellert/html/NN_Web_Pred_v9_JWE_preprint.pdf)); [GoWeB](https://arxiv.org/pdf/2108.03350) on Edge logs; Chrome omnibox ML scoring ([Google, 2024](https://blog.google/chromium/how-machine-learning-improved-chrome/)) | Proof that history predicts the next destination | Predicts habit, which is the opposite of "ought" | | History to interest profile | Chrome [Topics API](https://privacysandbox.google.com/private-advertising/topics/web): hostnames to 22 root topics, top 5 per week. [Being removed](https://groups.google.com/a/chromium.org/g/blink-dev/c/_R85yctz4Rs/m/CkQpJil0AQAJ) in 2026 | A production design for compressing history | Deliberately coarse, built for ads | | Sequential recommendation | [GRU4Rec](https://arxiv.org/pdf/1511.06939), SASRec, BERT4Rec ([replicability study](https://arxiv.org/pdf/2207.07483)), [HSTU](https://proceedings.mlr.press/v235/zhai24a.html) (Meta, 1.5T parameters in production), [TIGER](https://proceedings.neurips.cc/paper/2023/hash/20dcab0f14046a5c6b02b61da9f13229-Abstract.html) | Architectures for next-item prediction | Need a shared catalogue and millions of users. One person's web has an open item space and thousands of events | | LLM recommenders | [TALLRec](https://arxiv.org/abs/2305.00447) (works with under 100 labels), [LLaRA](https://arxiv.org/html/2312.02445v4), [USER-LLM](https://research.google/blog/user-llm-efficient-llm-contextualization-with-user-embeddings/) (compresses long history to 32 soft tokens), [ONCE](https://ar5iv.arxiv.org/html/2305.06566) (GPT infers a profile from news history) | The building blocks for an n=1 ranker | Trained and evaluated on pooled users | | News click data with dwell | [MIND](https://www.microsoft.com/en-us/research/?p=676704), [EB-NeRD](https://arxiv.org/pdf/2410.03432) (dwell and scroll depth) | Public data for pretraining a ranker or testing label noise | News only, other people | | Web navigation agents | [WebShop](https://arxiv.org/abs/2207.01206v2), [Mind2Web](https://arxiv.org/abs/2306.06070v1), [WebArena](https://arxiv.org/abs/2307.13854v4), [WebLINX](https://proceedings.mlr.press/v235/lu24e.html), [Online-Mind2Web](https://arxiv.org/abs/2504.01382v1) | The research agent a personal ranker would steer | Optimize for finishing tasks, not for taste | | Imitating real browsing people | [OPeRA](https://arxiv.org/pdf/2506.05606) (2025: 51 real users, about 4 weeks of Amazon logs with in-the-moment rationales and personas); [Lu et al.](https://arxiv.org/html/2503.20749v7) (3,526 users) | The closest data and evaluation recipe | Exact next action is hard: 12 to 22% accuracy. Rationales help. No per-user model | | Personal LLMs and digital twins | [LaMP](https://arxiv.org/abs/2304.11406v4), [PRISM](https://arxiv.org/pdf/2404.16019); [Park et al.](https://arxiv.org/abs/2411.10109v1) (1,052 interview-based agents reach 85% of people's own test-retest consistency); [PRISM-X](https://arxiv.org/pdf/2605.13307) (2026: per-user tuning gains small) | Evidence that individual imitation works on attitudes and is hard on preferences | Built from interviews, chat or writing, not browsing | | Personalized web agents | [PersonalWAB](https://arxiv.org/html/2410.17236v2) (purchase histories); [Persona2Web](https://arxiv.org/html/2602.17003v1) (2026, synthetic year-long browsing histories) | A benchmark shape for "agent conditioned on my history" | Persona2Web agents reach at most about 13%. Inferring preferences from implicit history is the hard part, and explicit profiles help most | Two things are worth knowing beyond the table: - The only n=1 browsing models I found are from the 2000s. [Brank et al.](https://www.microsoft.com/en-us/research/wp-content/uploads/2004/11/tr-2004-122.pdf) (MSR 2004, 30 users) predicted per-user habitual trails. [Lettkeman et al.](https://mlanthology.org/aaai/2006/lettkeman2006aaai-predicting) (AAAI 2006) predicted per-task revisits. Both were small, and association rules failed on individual logs. - [Compass](https://arxiv.org/abs/2608.04274) (August 2026) is the nearest feed-side work. An LLM realigns a YouTube Shorts feed to the user's reflective preferences, with prompts during browsing. A 10-day study with 15 users reported more purposeful use. The gap has three parts. First, a model of one person's long-term open-web history evaluated as a ranker for an agent. Second, a split between "would click" and "endorse" for a personal tab queue. Third, any shareable per-person model. I tried the wordings personal, single-user, n-of-1, tab recommendation, revisitation and read-later. This looks like a real gap, not a search miss. --- Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space