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.
Paper addresses selection bias in online advertising auctions.
Optimizes revenue and performance goals in e-commerce advertising with budget constraints.
Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to …
Real-time bidding (RTB) systems, which utilize auctions to allocate user impressions to competing advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of such advertising remains a challenge in research and practice. This paper proposes a new approach to perform causal inference on…
Develops a new bidding system to maximize advertiser profit.
Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.
Paper addresses CPA line forecasting in online advertising mid-flight.
A new method for precise user targeting in advertising using hyperbolic manifold learning.
In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the …
This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.
Comparison Lift uses bandit algorithms to optimize online ad testing.
Improved conversion rate prediction in online advertising using self-supervised pre-training.
A PID-based feedback-control system improves multiple KPIs in RTB display advertising.
Paper improves privacy-preserving measurement of advertising incrementality.
FairJob dataset for job recommendations in advertising, preserving fairness and utility.
Study proposes a time-aware model to predict user conversion intent.
System filters inappropriate YouTube content for advertisers.
DSPN predicts advertiser satisfaction and intent for e-commerce platforms.
We study the problem of adaptive control of a high dimensional linear quadratic (LQ) system. Previous work established the asymptotic convergence to an optimal controller for various adaptive control schemes. More recently, for the average cost LQ problem, a regret bound of was shown, apart form logarit…
The paper introduces SuccessProbaMax to optimize policy success probability in online advertising.
Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rel…
It takes skill to build a meaningful predictive model even with the abundance of implementations of modern machine learning algorithms and readily available computing resources. Building a model becomes challenging if hundreds of terabytes of data need to be processed to produce the training data set. In a digital adve…
Study optimal bidding strategies for digital ads targeting purchases and health campaigns.
User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most featu…
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal p…
New model uses symmetries and scaling laws to predict consumer advertising response.
In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance. As a result, ad event prediction systems are vital and widely used for sponsored search and display advertising as well as Real-Time Bidding (RTB). In this work, we introduce an enhanced …
The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the compa…
Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a str…
The traditional Sznajd model, as well as its Ochrombel simplification for opinion spreading, are applied to marketing with the help of advertising. The larger the lattice is the smaller is the amount of advertising needed to convince the whole market
The paper develops a method to evaluate recommendation systems without A/B testing.
The paper models user-advertiser interactions using point processes.
The paper explores how to measure and optimize ad reach while maintaining user privacy.
Study proposes a machine learning method for bid shading in first-price auctions.
Paper proposes a GPU-based system for training massive deep learning models in ads systems.
Study predicts ad conversions based on URL embeddings.
The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising. …
Advertisement (abbreviated ad) options are a recent development in online advertising. Simply, an ad option is a first look contract in which a publisher or search engine grants an advertiser a right but not obligation to enter into transactions to purchase impressions or clicks from a specific ad slot at a pre-specifi…
Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clic…
Efficiently identifies promising hyperparameters for online learning models.
Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share similar if not exactly the same set of candidate products. Consumers' behaviors on the advertised results constitute part of the recommendatio…
Optimal bidding strategy for multi-platform ad auctions under budget constraints.
The paper tackles the issue of preferential attachment in targeted display advertising by developing domain-adaptation approaches.
Study of negative ads on social media during U.S. midterm elections.
New bid shading algorithm reduces costs by 55%.
New framework for online influencer selection considering cost constraints.
A simple Ising spin model which can describe the mechanism of advertising in a duopoly market is proposed. In contrast to other agent-based models, the influence does not flow inward from the surrounding neighbors to the center site, but spreads outward from the center to the neighbors. The model thus describes the spr…
BiCB combines traffic prediction and bidding optimization for live advertising.