Managing Risk of Bidding in Display Advertising

Zhang, Haifeng; Zhang, Weinan; Rong, Yifei; Ren, Kan; Li, Wenxin; Wang, Jun

doi:10.1145/3018661.3018701

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Brown and Hayes [11] state that the consumer is exposed to more than a thousand advertisements before going to bed. Zhang et al [12] state that in the case of financial market trading, online advertising is based on real-time bidding and uses an auction mechanism to automate media purchases. Therefore, how optimally the bids are submitted is crucial for increasing profit and return on investment.…”

Section: Current State Of the Adressed Problem At Home And Abroadmentioning

confidence: 99%

Analysis of the Czech Advertising Market and its Main Players

et al. 2022

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At a time of global crisis, also at a time of accelerated digitalisation of businesses, sensible investment in communication with their customers is becoming very topical. The paper focuses on the development of investments in Internet advertising in the period 2012-2020. The method used for the research was a comparative analysis, which showed that the amount of money invested in internet advertising increased every year and that, unlike other types of advertising, no year was recorded for internet advertising during the period under review, when the value of investments in a given year would be lower than the value in the previous year. The results show that online advertising is taking an increasing share of total advertising investment. Another finding is that television advertising maintains a dominant position in the advertising market and is not directly affected by the development of investment in Internet advertising. However, the growth of investment in Internet advertising has a direct impact on the volume of investment in print advertising and OOH (Out of Home) advertising. The given types of media showed the largest decrease in terms of market share and negatively copied the development of investments in Internet advertising. In the case of radio advertising, there was also a decrease, but its dependence on Internet advertising has not been proven. However, the question arises as to the development of investments in the coming years. It is possible to anticipate the continuing strengthening of the role of Internet advertising and it will also be interesting to see whether Internet advertising will jeopardize the position of TV advertising. The results of the research will help both researchers and companies in the process of their decision to invest in selected types of advertising.

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Section: Current State Of the Adressed Problem At Home And Abroadmentioning

confidence: 99%

Analysis of the Czech Advertising Market and its Main Players

et al. 2022

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“…In [2], the authors suggest time-dependent models would be appropriate for capturing the repeated patterns and current bidding strategies are far less optimal, indicating the significant needs for optimization algorithms incorporating the facts such as the temporal behaviors, the frequency and recency of the ad displays. The authors of [7] proposed a bidding strategy managing risk in display advertising, i.e., the randomness of the user behaviors and the cost uncertainty. In recent years, reinforcement learning algorithms are widely applied to bidding strategies [8], [9].…”

Section: Related Work a Bidding Strategymentioning

confidence: 99%

Bid-Aware Active Learning in Real-Time Bidding for Display Advertising

Liu

2020

IEEE Access

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In Real-time Bidding (RTB) based display advertising, demand side platforms (DSPs) estimate the click-through rate (CTR) of each advertisement impression, and then decide whether and how much to bid based on the information of the user and the advertiser. Typically, when a new campaign is launched, the CTR estimation module of the DSP needs to collect data to train an accurate estimator. The advertiser is charged for each ad impression in display advertising, therefore there is some cost for obtaining each training instance. Thus one crucial task is to actively train an accurate CTR estimator within the constraint of the budget. Traditional active learning algorithms fail to deal with such scenario because (i) acquiring training instances is implemented via performing real-time bidding for the corresponding auctions; (ii) RTB requires the bidding agent to make real-time decisions for sequentially coming bid requests; (iii) cost for each ad impression will be unveiled only after giving the bid price and winning the auction; (iv) training data gathered in post-bid stage has a strong bias towards the won impressions. In this paper, we propose a Bid-aware Active Real-time Bidding (BARB) algorithm to actively choose training instances by setting different bid prices for each ad auction, in order to efficiently train an accurate CTR estimation model within the budget constraint. The empirical study on different campaigns of three real-world datasets with three budget constraints shows the effectiveness of our proposed algorithm. INDEX TERMS Real-time bidding, active learning, user response prediction.

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“…Real-time advertising generates large amounts of data over time. Globally, DSP Fikisu claims to process 32 billion ad impressions daily [Zhang et al, 2017] and DSP Turn reports to handle 2.5 million per second at peak time [Shen et al, 2015]. The New York Stock Exchange, to better envision the scale, trades around 12 billion shares daily.…”

Section: The Major Technical Challenges and Issuesmentioning

confidence: 99%

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

Wang

Zhang

Yuan³

2017

FNT in Information Retrieval

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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 user's 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.

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Managing Risk of Bidding in Display Advertising

Cited by 8 publications

References 35 publications

Analysis of the Czech Advertising Market and its Main Players

Analysis of the Czech Advertising Market and its Main Players

Bid-Aware Active Learning in Real-Time Bidding for Display Advertising

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

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