ECIR16 Tutorial on RTB display advertising

February 25th, 2016 Comments off

Our latest tutorial on RTB in ECIR16.  In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. The ability of making impression level bid decision and targeting to an individual user in real-time has fundamentally changed the landscape of the digital media. The further demand for automation, integration and optimisation in RTB brings new research opportunities in the IR fields, including information matching with economic constraints, CTR prediction, user behaviour targeting and profiling, personalised advertising, and attribution and evaluation methodologies. In this tutorial, teamed up with presenters from both the industry and academia, we aim to bring the insightful knowledge from the real-world systems, and to provide an overview of the fundamental mechanism and algorithms with the focus on the IR context. We will also introduce to IR researchers a few datasets recently made available so that they can get hands-on quickly and enable the said research.
details/link to the tutorial materials
http://tutorial.computational-advertising.org/

Post to Twitter

Categories: Computational Advertising, ECIR2016 Tags:

ECIR16 on our early results of Deep learning to estimating user responses (clicks/conversions)

February 25th, 2016 Comments off

Predicting user responses, such as click-through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known. Major user response prediction models have to either limit themselves to linear models or require manually building up high-order combination features. The former loses the ability of exploring feature interactions, while the latter results in a heavy computation in the large feature space. To tackle the issue, we propose two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users’ ad clicks. To get our DNNs efficiently work, we propose to leverage three feature transformation methods, i.e., factorisation machines (FMs), restricted Boltzmann machines (RBMs) and denoising auto-encoders (DAEs). This paper presents the structure of our models and their efficient training algorithms. The large-scale experiments with real-world data demonstrate that our methods work better than major state-of-the-art models.

Post to Twitter

Categories: Computational Advertising, ECIR2016 Tags:

ECIR16 Paper on Audience Lookalike modelling

February 25th, 2016 Comments off

User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users’ interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user’s interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users’ ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.

http://arxiv.org/abs/1601.02377

Post to Twitter

Categories: Computational Advertising, ECIR2016 Tags:

ACM WSDM16 Paper: Feedback Control in RTB advertising

February 25th, 2016 Comments off

Real-Time Bidding (RTB) is revolutionising display advertising by facilitating per-impression auctions to buy ad impressions as they are being generated. Being able to use impression-level data, such as user cookies, encourages user behaviour targeting, and hence has significantly improved the effectiveness of ad campaigns. However, a fundamental drawback of RTB is its instability because the bid decision is made per impression and there are enormous in campaigns’ key performance indicators (KPIs). As such, advertisers face great difficulty in controlling their campaign performance against the associated costs. In this paper, we propose a feedback control mechanism for RTB which helps advertisers dynamically adjust the bids to effectively control the KPIs, e.g., the auction winning ratio and the effective cost per click. We further formulate an optimisation framework to show that the proposed feedback control mechanism also has the ability of optimising campaign performance. By settling the effective cost per click at an optimal reference value, the number of campaign’s ad clicks can be maximised with the budget constraint. Our empirical study based on real-world data verifies the effectiveness and robustness of our RTB control system in various situations. The proposed feedback control mechanism has also been deployed on a commercial RTB platform and the online test has shown its success in generating controllable advertising performance.

Post to Twitter

Categories: Uncategorized Tags:

ACM KDD15 Paper: Statistical Arbitrage Mining for Real-Time Bidding based Display Advertising

May 13th, 2015 Comments off

We consider the problems of arbitrages in real-time bidding based display advertising. On a display ads trading desk, some advertisers would just pay per click or even pay per conversion so as to minimise their risk. In such cases, there is usually no delivery constraint since advertisers already have no risk. From the perspective of the trading desk, it tries to earn the user clicks or conversions in pay-per-view spot market via real-time bidding. It is possible for the trading desk to find some cost-effective cases to earn each click or conversion with a cost lower than the advertiser’s predefined cost. In such case, the arbitrage happens: the trading desk earn the difference of cost between the advertiser’s predefined cost for each click/conversion and the real cost on it, while advertisers get the user clicks or conversions with no risk. Such click/conversoin based transactions act as a complementary role to the mainstream guaranteed delivery and RTB spot market, and is a win-win game if the trading desk would successfully find the arbitrages. To the best of our knowledge, there is no literature on any automatic arbitrage techniques in RTB display advertising. In this paper, we propose a risk management real-time bidding model to find the arbitrage between advertisers’ CPC/CPA orders and the market situations.

http://www0.cs.ucl.ac.uk/staff/Weinan.Zhang/papers/sam-kdd.pdf

Post to Twitter

Categories: ACM KDD15 Tags:

Yahoo Faculty Research and Engagement Award 2014

July 24th, 2014 Comments off
Our proposal “Long-term Portfolio Optimisation and Game-Theoretic Analysis for Native Ads” has been awarded by the Yahoo Faculty Research and Engagement programme.
Native Advertising is a new form of online advertising that blends online content and advertising (ad) seamlessly. As ads are now integrated with the content, any unmatched ads or less  Because of such naturally blending, it departs significantly from traditional display and sponsored search ads and provides a natural way of enriching the user’s experience and delivers better returns for brands.
The goal of this research project is to model the “combinatorial” effect of the native ads and study its impact on incentives, user experiences, revenue optimisation, and market mechanism design. We propose to use Stochastic Portfolio Theory as a mathematical ground for studying the “combinatorial” effect of user engagements and its correlation with long-term revenue. As there are various participants (publishers, users and advertisers) involved and each of them has different objectives, a game theoretical analysis will be conducted to study the incentives and goal optimisation. The study is expected to result in 1) a better click-through estimation algorithm that can combine the contexts and the interactions among various participants. 2) The modelling of long-term revenue would result in an optimisation algorithm for designing the marketplace that takes into account the quality assessment of the blending and the incentives from various participants.
For more information, please refer to here.

Post to Twitter

Categories: Uncategorized Tags:

AdKDD2014: A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising

July 24th, 2014 Comments off

There are two major ways of selling impressions in display advertising. They are either sold in spot through auction mechanisms or in advance via guaranteed contracts. The former has achieved a significant automation via real-time bidding (RTB); however, the latter is still mainly done over the counter through direct sales. This paper proposes a mathematical model that allocates and prices the future impressions between real-time auctions and guaranteed contracts. Under conventional economic assumptions, our model shows that the two ways can be seamless combined programmatically and the publisher’s revenue can be maximized via price discrimination and optimal allocation. We consider advertisers are risk-averse, and they would be willing to purchase guaranteed impressions if the total costs are less than their private values. We also consider that an advertiser’s purchase behavior can be affected by both the guaranteed price and the time interval between the purchase time and the impression delivery date. Our solution suggests an optimal percentage of future impressions to sell in advance and provides an explicit formula to calculate at what prices to sell. We find that the optimal guaranteed prices are dynamic and are non-decreasing over time. We evaluate our method with RTB datasets and find that the model adopts different strategies in allocation and pricing according to the level of competition. From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB. In a highly competitive market, advertisers are more willing to purchase the guaranteed con- tracts and thus higher prices are expected. The revenue gain is largely contributed by the guaranteed selling.

http://arxiv.org/pdf/1405.5189v2

Post to Twitter

Categories: Computational Advertising, kdd2014 Tags:

ACM KDD2014 Paper: Optimal Real-Time Bidding for Display Advertising

July 24th, 2014 Comments off

In this paper we study bid optimisation for real-time bid- ding (RTB) based display advertising. RTB allows adver- tisers to bid on a display ad impression in real time when it is being generated. It goes beyond contextual advertising by motivating the bidding focused on user data and it is different from the sponsored search auction where the bid price is associated with keywords. For the demand side, a fundamental technical challenge is to automate the bid- ding process based on the budget, the campaign objective and various information gathered in runtime and in history. In this paper, the programmatic bidding is cast as a functional optimisation problem. Under certain dependency assumptions, we derive simple bidding functions that can be calculated in real time; our finding shows that the optimal bid has a non-linear relationship with the impression level evaluation such as the click-through rate and the conversion rate, which are estimated in real time from the impression level features. This is different from previous work that is mainly focused on a linear bidding function. Our mathematical derivation suggests that optimal bidding strategies should try to bid more impressions rather than focus on a small set of high valued impressions because ac- cording to the current RTB market data, compared to the higher evaluated impressions, the lower evaluated ones are more cost effective and the chances of winning them are relatively higher. Aside from the theoretical insights, offline experiments on a real dataset and online experiments on a production RTB system verify the effectiveness of our proposed optimal bidding strategies and the functional optimisation framework.

the paper is downloadable at here.

Post to Twitter

Categories: Computational Advertising, kdd2014 Tags:

ACM KDD2014 Paper: An Empirical Study of Reserve Price Optimisation in Real-Time Bidding

July 24th, 2014 Comments off

In this paper we analyse the reserve price optimisation problem and report the first empirical study in the Real-Time Bidding (RTB) context. A reserve price is the minimum that the publisher would accept from bidders in auctions and it is one of the most important yield management tools. In the second price auction it could uplift revenue by charging winners the reserve price instead of the second highest bids. Comparing with other popular forms (sponsored search, contextual advertising, etc.), this problem in the RTB context has unique features, mainly because bidders have to submit a bid for each individual impression and using user data is greatly encouraged, as well as many practical constraints (budget, campaigns’ life time, etc.) and irrational decisions. These make inferring the private values very difficult thus make it interesting and necessary to examine commonly adopted algorithms in the real-world. Moreover, in RTB an advertiser is facing nearly unlimited supply which naturally encourages spending less on placements of high cost. This could imply the loss of bid volume over time if an aggressive reserve price is in place, which requires analysis on bidders’ attrition. In this paper we empirically study commonly adopted algorithms at placement level and propose variations. Especially we analyse challenge of fitting private values, which are foundation of the optimal auction theory, in RTB practice based on real-world data. Then, we represent our findings on RTB auctions and discuss their possible impact. Finally, we report a large scale online experiment in a production platform. The results suggest the game theory based OneShot policy performed the best and the superiority is significant in most cases. The analysis on bidders’ attrition show good chances for publishers to active this optimisation.

a copy can be downloaded here.

Post to Twitter

Categories: Computational Advertising, kdd2014 Tags:

iPinYou the first global RTB (Real-Time Bidding) algorithm competition

December 29th, 2013 Comments off
Offline Evaluation Result of iPinYou Bidding Algorithm Competition

Offline Evaluation Result of iPinYou Bidding Algorithm Competition

We are involved in the first global RTB bidding algorithm competition, organised by iPinYou. Our Phd Students Weinan Zhang and Shuai Yuan have won the third season offline competition. The result can be found here. In this season, data scientists from eight countries, including China, USA, UK, India and France, have participated in the competition and made over 2000 submissions. The IPinYou global RTB (Real-Time Bidding) algorithm competition kicks off on April 1, 2013. The purpose of this competition is to further improve the performance of DSP bidding algorithm, stimulate the interest of research and development of DSP bidding algorithms in the whole data science research community, and speed up the growth of RTB-enabled display advertising ecosystem. In the end, iPinYou expects this competition will help advertising technology companies serve our advertisers better. There are three Milestone Prizes and one Grand Prize (RMB 1,000,000) for the DSP bidding competition.

Post to Twitter

Categories: Computational Advertising Tags: