Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count% of corpus
0111 · May 202319922001200920172026
6 results for “TikTok”
User Strategization and Trustworthy Algorithmscs.CY

User strategization undermines algorithmic trustworthiness.

problem User strategic behavior corrupts algorithmic data and trust.
method Modeling user-platform interactions as a game, analyzing strategic behavior's short-term benefits and long-term harms.
result User strategization can initially benefit platforms but ultimately harms their ability to make accurate decisions.
algorithmic trustuser behaviorstrategic interactionrecommender systemsdata-driven platforms
2023-12-29abs ↗pdf ↗
Incentivizing High-Quality Content in Online Recommender Systemscs.GT

Study shows online learning algorithms incentivize low-quality content, proposing new algorithms to improve quality.

problem Online learning algorithms in content recommender systems incentivize producers to create low-quality content.
method Analyzed the game between producers and content quality, designed new learning algorithms to incentivize high effort and quality.
result New algorithms incentivize producers to invest high effort and achieve high user welfare, improving content quality.
content recommender systemsonline learningincentivizationhigh-quality contentuser welfare
2023-06-13abs ↗pdf ↗
Causal Inference on Stopped Random Walks in Online Advertisingstat.ML

Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.

problem Estimating long-term treatment effects in online advertising systems.
method Modeling treatment effects as a stopped random walk, using experimental design, and statistical methods.
result Confidence intervals for long-term treatment effects constructed using statistical techniques.
online advertisingcausal inferencerandom walkexperimental designconfidence intervals
2026-02-05abs ↗pdf ↗
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contextscs.LG

New algorithm poLinUCB improves online learning in content recommendation platforms.

problem Improving efficiency in content recommendation platforms with post-serving context.
method Novel contextual bandit problem with post-serving contexts and a new algorithm, poLinUCB.
result Achieves tight regret under standard assumptions and significant benefit of utilizing post-serving contexts.
contextual banditscontent recommendationpost-serving contexttight regretElliptical Potential Lemma
2023-09-25abs ↗pdf ↗