5,649 research outputs found
Sequential Selection of Correlated Ads by POMDPs
Online advertising has become a key source of revenue for both web search
engines and online publishers. For them, the ability of allocating right ads to
right webpages is critical because any mismatched ads would not only harm web
users' satisfactions but also lower the ad income. In this paper, we study how
online publishers could optimally select ads to maximize their ad incomes over
time. The conventional offline, content-based matching between webpages and ads
is a fine start but cannot solve the problem completely because good matching
does not necessarily lead to good payoff. Moreover, with the limited display
impressions, we need to balance the need of selecting ads to learn true ad
payoffs (exploration) with that of allocating ads to generate high immediate
payoffs based on the current belief (exploitation). In this paper, we address
the problem by employing Partially observable Markov decision processes
(POMDPs) and discuss how to utilize the correlation of ads to improve the
efficiency of the exploration and increase ad incomes in a long run. Our
mathematical derivation shows that the belief states of correlated ads can be
naturally updated using a formula similar to collaborative filtering. To test
our model, a real world ad dataset from a major search engine is collected and
categorized. Experimenting over the data, we provide an analyse of the effect
of the underlying parameters, and demonstrate that our algorithms significantly
outperform other strong baselines
Online Model Evaluation in a Large-Scale Computational Advertising Platform
Online media provides opportunities for marketers through which they can
deliver effective brand messages to a wide range of audiences. Advertising
technology platforms enable advertisers to reach their target audience by
delivering ad impressions to online users in real time. In order to identify
the best marketing message for a user and to purchase impressions at the right
price, we rely heavily on bid prediction and optimization models. Even though
the bid prediction models are well studied in the literature, the equally
important subject of model evaluation is usually overlooked. Effective and
reliable evaluation of an online bidding model is crucial for making faster
model improvements as well as for utilizing the marketing budgets more
efficiently. In this paper, we present an experimentation framework for bid
prediction models where our focus is on the practical aspects of model
evaluation. Specifically, we outline the unique challenges we encounter in our
platform due to a variety of factors such as heterogeneous goal definitions,
varying budget requirements across different campaigns, high seasonality and
the auction-based environment for inventory purchasing. Then, we introduce
return on investment (ROI) as a unified model performance (i.e., success)
metric and explain its merits over more traditional metrics such as
click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss
commonly used evaluation and metric summarization approaches in detail and
propose a more accurate method for online evaluation of new experimental models
against the baseline. Our meta-analysis-based approach addresses various
shortcomings of other methods and yields statistically robust conclusions that
allow us to conclude experiments more quickly in a reliable manner. We
demonstrate the effectiveness of our evaluation strategy on real campaign data
through some experiments.Comment: Accepted to ICDM201
Audience Prospecting for Dynamic-Product-Ads in Native Advertising
With yearly revenue exceeding one billion USD, Yahoo Gemini native
advertising marketplace serves more than two billion impressions daily to
hundreds of millions of unique users. One of the fastest growing segments of
Gemini native is dynamic-product-ads (DPA), where major advertisers, such as
Amazon and Walmart, provide catalogs with millions of products for the system
to choose from and present to users. The subject of this work is finding and
expanding the right audience for each DPA ad, which is one of the many
challenges DPA presents. Approaches such as targeting various user groups,
e.g., users who already visited the advertisers' websites (Retargeting), users
that searched for certain products (Search-Prospecting), or users that reside
in preferred locations (Location-Prospecting), have limited audience expansion
capabilities. In this work we present two new approaches for audience expansion
that also maintain predefined performance goals. The Conversion-Prospecting
approach predicts DPA conversion rates based on Gemini native logged data, and
calculates the expected cost-per-action (CPA) for determining users'
eligibility to products and optimizing DPA bids in Gemini native auctions. To
support new advertisers and products, the Trending-Prospecting approach matches
trending products to users by learning their tendency towards products from
advertisers' sites logged events. The tendency scores indicate the popularity
of the product and the similarity of the user to those who have previously
engaged with this product. The two new prospecting approaches were tested
online, serving real Gemini native traffic, demonstrating impressive DPA
delivery and DPA revenue lifts while maintaining most traffic within the
acceptable CPA range (i.e., performance goal). After a successful testing
phase, the proposed approaches are currently in production and serve all Gemini
native traffic.Comment: In Proc. IeeeBigData'2023 (Industry and Government Program
Prediction of Conversion Rates in Online Marketing - A study of the application of logistic regression for predicting conversion rates in online marketing.
This thesis was written in collaboration with an anonymous European automotive company, Company X, which uses online marketing as a part of their business model. In online marketing it is of inrest to estimate conversion rates, that is the quota of a population at an initial state that will go on to perform a certain action. The action could be, but is not limited to, clicking on an advertisement, interacting in a certain way with the advertisers webpage, or buying a product. If the advertiser can estimate the value of the performed action, and the conversion rate to the action, the advertiser can then calculate the value of the initial state. In extension, is means that if a company knows the life time value of a customer, and can estimate the conversion rate from someone clicking on one of their advertisements to becoming a customer, they can calculate the value of that click. Generally online marketing space is sold through auctions. Different companies bifor the same given advertising space depending on the expected value of the space and pay for exposure. Exposure is either measured in how many users that has seen the ad (impressions) or how many users that have interacted with the ad (usually measured in clicks). Due to this, if a company can improve the precision of how they estimate the value of an impression or click they can spend their online marketing budget more effectively. Considering the size and rapid growth of the online marketing market, this is of high interest. In this thesis a logistic regression modeling appach was compared to a group average approach for predicting conversion rates. The group average approach is based on grouping different advertisements that have few observations into bigger populations and then using the average of the bigger population. The thesis finds that in most cases logistic regression models seems preferable. However, when the variance of the conversion rates is large, the Group average model can be prefereble
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a userâs visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a userâs visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection
Supply Side Optimisation in Online Display Advertising
On the Internet there are publishers (the supply side) who provide free contents (e.g., news) and services (e.g., email) to attract users. Publishers get paid by selling ad displaying opportunities (i.e., impressions) to advertisers. Advertisers then sell products to users who are converted by ads. Better supply side revenue allows more free content and services to be created, thus, benefiting the entire online advertising ecosystem. This thesis addresses several optimisation problems for the supply side. When a publisher creates an ad-supported website, he needs to decide the percentage of ads first. The thesis reports a large-scale empirical study of Internet ad density over past seven years, then presents a model that includes many factors, especially the competition among similar publishers, and gives an optimal dynamic ad density that generates the maximum revenue over time. This study also unveils the tragedy of the commons in online advertising where users' attention has been overgrazed which results in a global sub-optimum. After deciding the ad density, the publisher retrieves ads from various sources, including contracts, ad networks, and ad exchanges. This forms an exploration-exploitation problem when ad sources are typically unknown before trail. This problem is modelled using Partially Observable Markov Decision Process (POMDP), and the exploration efficiency is increased by utilising the correlation of ads. The proposed method reports 23.4% better than the best performing baseline in the real-world data based experiments. Since some ad networks allow (or expect) an input of keywords, the thesis also presents an adaptive keyword extraction system using BM25F algorithm and the multi-armed bandits model. This system has been tested by a domain service provider in crowdsourcing based experiments. If the publisher selects a Real-Time Bidding (RTB) ad source, he can use reserve price to manipulate auctions for better payoff. This thesis proposes a simplified game model that considers the competition between seller and buyer to be one-shot instead of repeated and gives heuristics that can be easily implemented. The model has been evaluated in a production environment and reported 12.3% average increase of revenue. The documentation of a prototype system for reserve price optimisation is also presented in the appendix of the thesis
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
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