11,366 research outputs found
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
Accurately predicting conversions in advertisements is generally a
challenging task, because such conversions do not occur frequently. In this
paper, we propose a new framework to support creating high-performing ad
creatives, including the accurate prediction of ad creative text conversions
before delivering to the consumer. The proposed framework includes three key
ideas: multi-task learning, conditional attention, and attention highlighting.
Multi-task learning is an idea for improving the prediction accuracy of
conversion, which predicts clicks and conversions simultaneously, to solve the
difficulty of data imbalance. Furthermore, conditional attention focuses
attention of each ad creative with the consideration of its genre and target
gender, thus improving conversion prediction accuracy. Attention highlighting
visualizes important words and/or phrases based on conditional attention. We
evaluated the proposed framework with actual delivery history data (14,000
creatives displayed more than a certain number of times from Gunosy Inc.), and
confirmed that these ideas improve the prediction performance of conversions,
and visualize noteworthy words according to the creatives' attributes.Comment: 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 2019) as an applied data science
pape
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
Bid Optimization by Multivariable Control in Display Advertising
Real-Time Bidding (RTB) is an important paradigm in display advertising,
where advertisers utilize extended information and algorithms served by Demand
Side Platforms (DSPs) to improve advertising performance. A common problem for
DSPs is to help advertisers gain as much value as possible with budget
constraints. However, advertisers would routinely add certain key performance
indicator (KPI) constraints that the advertising campaign must meet due to
practical reasons. In this paper, we study the common case where advertisers
aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a
KPI constraint. We convert such a problem into a linear programming problem and
leverage the primal-dual method to derive the optimal bidding strategy. To
address the applicability issue, we propose a feedback control-based solution
and devise the multivariable control system. The empirical study based on
real-word data from Taobao.com verifies the effectiveness and superiority of
our approach compared with the state of the art in the industry practices
Managing Risk of Bidding in Display Advertising
In this paper, we deal with the uncertainty of bidding for display
advertising. Similar to the financial market trading, real-time bidding (RTB)
based display advertising employs an auction mechanism to automate the
impression level media buying; and running a campaign is no different than an
investment of acquiring new customers in return for obtaining additional
converted sales. Thus, how to optimally bid on an ad impression to drive the
profit and return-on-investment becomes essential. However, the large
randomness of the user behaviors and the cost uncertainty caused by the auction
competition may result in a significant risk from the campaign performance
estimation. In this paper, we explicitly model the uncertainty of user
click-through rate estimation and auction competition to capture the risk. We
borrow an idea from finance and derive the value at risk for each ad display
opportunity. Our formulation results in two risk-aware bidding strategies that
penalize risky ad impressions and focus more on the ones with higher expected
return and lower risk. The empirical study on real-world data demonstrates the
effectiveness of our proposed risk-aware bidding strategies: yielding profit
gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on
a commercial RTB platform over the widely applied bidding strategies
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