1,708 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
Whole-Page Optimization and Submodular Welfare Maximization with Online Bidders
In the context of online ad serving, display ads may appear on different types of webpages, where each page includes several ad slots and therefore multiple ads can be shown on each page. The set of ads that can be assigned to ad slots of the same page needs to satisfy various prespecified constraints including exclusion constraints, diversity constraints, and the like. Upon arrival of a user, the ad serving system needs to allocate a set of ads to the current webpage respecting these per-page allocation constraints. Previous slot-based settings ignore the important concept of a page and may lead to highly suboptimal results in general. In this article, motivated by these applications in display advertising and inspired by the submodular welfare maximization problem with online bidders, we study a general class of page-based ad allocation problems, present the first (tight) constant-factor approximation algorithms for these problems, and confirm the performance of our algorithms experimentally on real-world datasets.
A key technical ingredient of our results is a novel primal-dual analysis for handling free disposal, which updates dual variables using a “level function” instead of a single level and unifies with previous analyses of related problems. This new analysis method allows us to handle arbitrarily complicated allocation constraints for each page. Our main result is an algorithm that achieves a 1 &minus frac 1 e &minus o(1)-competitive ratio. Moreover, our experiments on real-world datasets show significant improvements of our page-based algorithms compared to the slot-based algorithms.
Finally, we observe that our problem is closely related to the submodular welfare maximization (SWM) problem. In particular, we introduce a variant of the SWM problem with online bidders and show how to solve this problem using our algorithm for whole-page optimization.postprin
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Sponsored search represents a major source of revenue for web search engines.
This popular advertising model brings a unique possibility for advertisers to
target users' immediate intent communicated through a search query, usually by
displaying their ads alongside organic search results for queries deemed
relevant to their products or services. However, due to a large number of
unique queries it is challenging for advertisers to identify all such relevant
queries. For this reason search engines often provide a service of advanced
matching, which automatically finds additional relevant queries for advertisers
to bid on. We present a novel advanced matching approach based on the idea of
semantic embeddings of queries and ads. The embeddings were learned using a
large data set of user search sessions, consisting of search queries, clicked
ads and search links, while utilizing contextual information such as dwell time
and skipped ads. To address the large-scale nature of our problem, both in
terms of data and vocabulary size, we propose a novel distributed algorithm for
training of the embeddings. Finally, we present an approach for overcoming a
cold-start problem associated with new ads and queries. We report results of
editorial evaluation and online tests on actual search traffic. The results
show that our approach significantly outperforms baselines in terms of
relevance, coverage, and incremental revenue. Lastly, we open-source learned
query embeddings to be used by researchers in computational advertising and
related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital
Stochastic Budget Optimization in Internet Advertising
Internet advertising is a sophisticated game in which the many advertisers
"play" to optimize their return on investment. There are many "targets" for the
advertisements, and each "target" has a collection of games with a potentially
different set of players involved. In this paper, we study the problem of how
advertisers allocate their budget across these "targets". In particular, we
focus on formulating their best response strategy as an optimization problem.
Advertisers have a set of keywords ("targets") and some stochastic information
about the future, namely a probability distribution over scenarios of cost vs
click combinations. This summarizes the potential states of the world assuming
that the strategies of other players are fixed. Then, the best response can be
abstracted as stochastic budget optimization problems to figure out how to
spread a given budget across these keywords to maximize the expected number of
clicks.
We present the first known non-trivial poly-logarithmic approximation for
these problems as well as the first known hardness results of getting better
than logarithmic approximation ratios in the various parameters involved. We
also identify several special cases of these problems of practical interest,
such as with fixed number of scenarios or with polynomial-sized parameters
related to cost, which are solvable either in polynomial time or with improved
approximation ratios. Stochastic budget optimization with scenarios has
sophisticated technical structure. Our approximation and hardness results come
from relating these problems to a special type of (0/1, bipartite) quadratic
programs inherent in them. Our research answers some open problems raised by
the authors in (Stochastic Models for Budget Optimization in Search-Based
Advertising, Algorithmica, 58 (4), 1022-1044, 2010).Comment: FINAL versio
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