advertising platform operators

Abstract: Advertising platforms subsidize advertisers through billing discounts, and Advertiser Subsidy Allocation (ASA) chooses a subsidy level for every account to maximize total discounted revenue under a platform-wide subsidy-rate constraint. In practice, operators configure these subsidies from business rules, advertiser tiers and operating goals. Such configuration draws on rich business experience, yet as the advertiser base grows, tier-level policies can neither resolve how individual accounts respond across subsidy levels nor allocate a shared budget at the granularity of single accounts. We propose CASH, a Capability-Aware Subsidy Harness for ASA. CASH pairs a knowledge-guided large language model (LLM) with a data-driven numerical response model, learns from realized outcomes where each is locally reliable across accounts and subsidy levels, and turns this reliability into explicit capability licenses that govern source selection, agent review and budget allocation. Its Expected Policy Coverage Gain (EPCG) directs a limited feedback budget toward evidence expected to expand high-value reliable coverage, and the resulting feedback further improves the numerical model and updates capability estimates. In a one-month online experiment with CASH serving 60% of a large-scale advertising platform's traffic, CASH raises revenue per account by 15.35% and ADVV by 19.86% over Control at an essentially unchanged subsidy rate, with further gains in revenue quality, LTV and retention.

Abstract: Public advertising transparency relies on a minimum accountability condition: an account authorized by a publisher in ads.txt or app-ads.txt should appear in the applicable sellers.json registry. When an available registry omits the declared seller ID, the authorization remains visible and machine-readable, but its public verification path breaks. We call this failure the seller-identity gap. We measure the gap using 233.4 million normalized advertising-transparency records and an absent-only, coverage-aware evidence rule. A declaration enters the numerator only when the applicable registry is available and omits its seller ID; unavailable, malformed, confidential, and other non-evaluable states remain separate. The resulting row-level floor is 24.25% in a high-popularity, low-removal reference population and 30.71% among publishers associated with at least 100 copyright-removal requests, a 6.45 percentage-point increase. The gap then remains near one third at stricter cumulative thresholds. Standardizing the populations to common weights over 1,469 shared ad systems reduces the primary contrast to 4.65 points: about 72% remains within systems, while 28% forms a composition-associated residual. We connect these population estimates to recurring seller-account coordinates, byte-identical declaration configurations, and provenance-bearing case packets. We frame this declaration-centered investigative use of public advertising records as Ad Transparency Intelligence: it reconstructs declared supply-chain relationships, identifies where public verification stops, and prioritizes evidence for corroboration without equating correlation with attribution. Advertising transparency supports accountability and investigation only when visible authorizations remain publicly verifiable.

Abstract: We study randomized algorithms for the fully online allocation of indivisible goods among $n\ge2$ agents with nonnegative additive valuations. Goods arrive one by one and must be allocated immediately and irrevocably. Nothing is known in advance except the number of agents. Since exact ex-ante envy freeness and proportionality are readily achievable, while no positive ex-post approximation is possible for the fairness notions considered here, we study the intermediate notions of high-probability fairness and expected realized fairness. Against a non-adaptive adversary, we give a randomized algorithm that achieves an $Ω(\sqrt{\log n}/\log(n/δ))$-approximation to proportionality up to one good (PROP1) with probability at least $1-δ$ for every $δ\in(0,1)$, and an expected realized PROP1 factor of $Ω(1/\sqrt{\log n})$. Compared with independent uniform allocation (Rand), these guarantees yield an $Ω(\sqrt{\log n})$ improvement under both the high-probability and expected realized criteria. For envy freeness up to any good (EFX), the success probability of any positive factor can be made arbitrarily small, even for identical valuations. Consequently, the expected realized factor of every randomized fully online algorithm is zero. For envy freeness up to one good (EF1), no randomized fully online algorithm can guarantee a positive factor with confidence exceeding $\frac{n+1}{2n}$. The expected realized EF1 guarantee is also at most $\frac{n+1}{2n}$.

Abstract: Multi-route recommender systems combine retrieval, allocation, fusion, and ranking, making individual inclusions and exclusions difficult to audit. Route overlap can hide effects from one-at-a-time ablations, while freezing downstream stages produces counterfactuals inconsistent with serving behavior. We introduce FunnelAudit, an executable framework for incident-level responsibility auditing. An accountability contract specifies the disputed Top-K event, controls and owners, permitted reference actions, and replay semantics. FunnelAudit evaluates every permitted control configuration and applies graded actual responsibility to find the smallest outcome-preserving contingency that makes each control pivotal. Its certificate records the contingency and paired serving executions needed to verify the judgment. We instantiate the framework in two-stage, nine-route funnels using fixed union, weighted quota allocation, or weighted reciprocal-rank fusion, followed by SASRec ranking. Across 258,809 user-target incidents from three real interaction datasets, 4.24-16.24% admit a responsible control. Among responsible incident-control pairs, 92.55-99.64% require a nonempty contingency, so single-control ablation recovers only 0.36-7.45%. Policies differing in factual outcomes on only 0.31-2.39% of incidents yield 21.44-54.05% Jaccard distance between responsible-route sets on matched exclusions. Independent replay reproduces all 9,121,792 checked target-world outcomes; exhaustive search and a generic mixed-integer linear program agree with every sampled judgment. These findings demonstrate the importance of explicit serving semantics and checkable witnesses for recommender accountability.