130 research outputs found
Learning Adaptive Display Exposure for Real-Time Advertising
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 platform revenue and user experience. Consequently, major
e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible
ways to display ads. In this paper, we investigate the problem of advertising
with adaptive exposure: can we dynamically determine the number and positions
of ads for each user visit under certain business constraints so that the
platform revenue can be increased? More specifically, we consider two types of
constraints: request-level constraint ensures user experience for each user
visit, and platform-level constraint controls the overall platform monetization
rate. We model this problem as a Constrained Markov Decision Process with
per-state constraint (psCMDP) and propose a constrained two-level reinforcement
learning approach to decompose the original problem into two relatively
independent sub-problems. To accelerate policy learning, we also devise a
constrained hindsight experience replay mechanism. Experimental evaluations on
industry-scale real-world datasets demonstrate the merits of our approach in
both obtaining higher revenue under the constraints and the effectiveness of
the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201
PREDICTING ONLINE USER BEHAVIOR BASED ON REAL-TIME ADVERTISING DATA
Generating economic value from big data is a challenge for many companies these days. On the Internet, a major source of big data is structured and unstructured data generated by users. Companies can use this data to better understand patterns of user behavior and to improve marketing decisions. In this paper, we focus on data generated in real-time advertising where billions of advertising slots are sold by auction. The auctions are triggered by user activity on websites that use this form of advertising to sell their advertising slots. During an auction, so-called bid requests are sent to advertisers who bid for the advertising slots. We develop a model that uses bid requests to predict whether a user will visit a certain website during his or her user journey. These predictions can be used by advertisers to derive user interests early in the sales funnel and, thus, to increase profits from branding campaigns. By iteratively applying a Bayesian multinomial logistic model to data from a case study, we show how to constantly improve the predictive accuracy of the model. We calculate the economic value of our model and show that it can be beneficial for advertisers in the context of cross-channel advertising
Statistical modelling of clickstream behaviour to inform real-time advertising decisions
Online user browsing generates vast quantities of typically unexploited data. Investigating this data and uncovering the valuable information it contains can be of substantial value to online businesses, and statistics plays a key role in this process.
The data takes the form of an anonymous digital footprint associated with each unique visitor, resulting in unique profiles across individual page visits on a daily basis. Exploring, cleaning and transforming data of this scale and high dimensionality (2TB+ of memory) is particularly challenging, and requires cluster computing.
We outline a variable selection method to summarise clickstream behaviour with a single value, and make comparisons to other dimension reduction techniques. We illustrate how to apply generalised linear models and zero-inflated models to predict sponsored search advert clicks based on keywords.
We consider the problem of predicting customer purchases (known as conversions), from the customer’s journey or clickstream, which is the sequence of pages seen during a single visit to a website. We consider each page as a discrete state with probabilities of transitions between the pages, providing the basis for a simple Markov model.
Further, Hidden Markov models (HMMs) are applied to relate the observed clickstream to a sequence of hidden states, uncovering meta-states of user activity. We can also apply conventional logistic regression to model conversions in terms of summaries of the profile’s browsing behaviour and incorporate both into a set of tools to solve a wide range of conversion types where we can directly compare the predictive capability of each model.
In real-time, predicting profiles that are likely to follow similar behaviour patterns to known conversions, will have a critical impact on targeted advertising. We illustrate these analyses with results from real data collected by an Audience Management Platform (AMP) - Carbon
Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems
"Creativity is the heart and soul of advertising services". Effective
creatives can create a win-win scenario: advertisers can reach target users and
achieve marketing objectives more effectively, users can more quickly find
products of interest, and platforms can generate more advertising revenue. With
the advent of AI-Generated Content, advertisers now can produce vast amounts of
creative content at a minimal cost. The current challenge lies in how
advertising systems can select the most pertinent creative in real-time for
each user personally. Existing methods typically perform serial ranking of ads
or creatives, limiting the creative module in terms of both effectiveness and
efficiency. In this paper, we propose for the first time a novel architecture
for online parallel estimation of ads and creatives ranking, as well as the
corresponding offline joint optimization model. The online architecture enables
sophisticated personalized creative modeling while reducing overall latency.
The offline joint model for CTR estimation allows mutual awareness and
collaborative optimization between ads and creatives. Additionally, we optimize
the offline evaluation metrics for the implicit feedback sorting task involved
in ad creative ranking. We conduct extensive experiments to compare ours with
two state-of-the-art approaches. The results demonstrate the effectiveness of
our approach in both offline evaluations and real-world advertising platforms
online in terms of response time, CTR, and CPM.Comment: 9 pages, 4 figures, AAAI202
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
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 strategic response with respect to other advertisers bidding in the
market. In this paper, we formulate bidding optimization with multi-agent
reinforcement learning. To deal with a large number of advertisers, we propose
a clustering method and assign each cluster with a strategic bidding agent. A
practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed
and implemented to balance the tradeoff between the competition and cooperation
among advertisers. The empirical study on our industry-scaled real-world data
has demonstrated the effectiveness of our methods. Our results show
cluster-based bidding would largely outperform single-agent and bandit
approaches, and the coordinated bidding achieves better overall objectives than
purely self-interested bidding agents
Fuzzy Content Mining for Targeted Advertisement
Content-targeted advertising system is becoming an increasingly important part of the funding source of free web services. Highly efficient content analysis is the pivotal key of such a system. This project aims to establish a content analysis engine involving fuzzy logic that is able to automatically analyze real user-posted Web documents such as blog entries. Based on the analysis result, the system matches and retrieves the most appropriate Web advertisements. The focus and complexity is on how to better estimate and acquire the keywords that represent a given Web document. Fuzzy Web mining concept will be applied to synthetically consider multiple factors of Web content. A Fuzzy Ranking System is established based on certain fuzzy (and some crisp) rules, fuzzy sets, and membership functions to get the best candidate keywords. Once it is has obtained the keywords, the system will retrieve corresponding advertisements from certain providers through Web services as matched advertisements, similarly to retrieving a products list from Amazon.com. In 87% of the cases, the results of this system can match the accuracy of the Google Adwords system. Furthermore, this expandable system will also be a solid base for further research and development on this topic
SAFE Newsletter : 2013, Q3
Research: Joachim Weber, Benjamin Loos, Steffen Meyer, Andreas Hackethal "Individual Investors' Trading Motives and Security Selling Behavior"
Ignazio Angeloni, Ester Faia "Monetary Policy and Prudential Regulations with Bank Runs"
Helmut Siekmann "Legal Limits to Quantitative Easing"
Policy Margit Vanberg "SAFE Summer Academy 2013 on 'International Financial Stability'"
Guest Commentary Peter Praet "Cooperation between the ECB and Academia
Pricing average price advertising options when underlying spot market prices are discontinuous
Advertising options have been recently studied as a special type of
guaranteed contracts in online advertising, which are an alternative sales
mechanism to real-time auctions. An advertising option is a contract which
gives its buyer a right but not obligation to enter into transactions to
purchase page views or link clicks at one or multiple pre-specified prices in a
specific future period. Different from typical guaranteed contracts, the option
buyer pays a lower upfront fee but can have greater flexibility and more
control of advertising. Many studies on advertising options so far have been
restricted to the situations where the option payoff is determined by the
underlying spot market price at a specific time point and the price evolution
over time is assumed to be continuous. The former leads to a biased calculation
of option payoff and the latter is invalid empirically for many online
advertising slots. This paper addresses these two limitations by proposing a
new advertising option pricing framework. First, the option payoff is
calculated based on an average price over a specific future period. Therefore,
the option becomes path-dependent. The average price is measured by the power
mean, which contains several existing option payoff functions as its special
cases. Second, jump-diffusion stochastic models are used to describe the
movement of the underlying spot market price, which incorporate several
important statistical properties including jumps and spikes, non-normality, and
absence of autocorrelations. A general option pricing algorithm is obtained
based on Monte Carlo simulation. In addition, an explicit pricing formula is
derived for the case when the option payoff is based on the geometric mean.
This pricing formula is also a generalized version of several other option
pricing models discussed in related studies.Comment: IEEE Transactions on Knowledge and Data Engineering, 201
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