Jon Feldman
Dr. Feldman graduated from Dartmouth College (BS, 97) and MIT (Ph.D., 03). He was an NSF postdoc at Columbia University before joining as a Research Scientist at Google, NY. His research has been in Algorithms, Coding Theory, and other areas of Theoretical Computer Science. Currently he is working on algorithms and systems for sponsored search advertising at Google.
Authored Publications
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Multiplicative Bidding in Online Advertising
Sam Chiu-wai Wong
ACM Conference on Economics and Computation (EC) (2014)
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In this paper, we initiate the study of the multiplicative bidding language adopted by major Internet search companies. In multiplicative bidding, the effective bid on a particular search auction is the product of a base bid and bid adjustments that are dependent on features of the search (for example, the geographic location of the user, or the platform on which the search is conducted). We consider the task faced by the advertiser when setting these bid adjustments, and establish a foundational optimization problem that captures the core difficulty of bidding under this language. We give matching algorithmic and approximation hardness results for this problem; these results are against an information-theoretic bound, and thus have implications on the power of the multiplicative bidding language itself. Inspired by empirical studies of search engine price data, we then codify the relevant restrictions of the problem, and give further algorithmic and hardness results. Our main technical contribution is an O(log n)-approximation for the case of multiplicative prices and monotone values. We also provide empirical validations of our problem restrictions, and test our algorithms on real data against natural benchmarks. Our experiments show that they perform favorably compare with the baseline.
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Reduce and aggregate: similarity ranking in multi-categorical bipartite graphs
Stefano Leonardi
WWW (2014), pp. 349-360
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We study the problem of computing similarity rankings in large-scale multi-categorical bipartite graphs, where the two sides of the graph represent actors and items, and the items are partitioned into an arbitrary set of categories. The problem has several real-world applications, including identifying competing advertisers and suggesting related queries in an online advertising system or finding users with similar interests and suggesting content to them. In these settings, we are interested in computing on-the-fly rankings of similar actors, given an actor and an arbitrary subset of categories of interest. Two main challenges arise: First, the bipartite graphs are huge and often lopsided (e.g. the system might receive billions of queries while presenting only millions of advertisers). Second, the sheer number of possible combinations of categories prevents the pre-computation of the results for all of them. We present a novel algorithmic framework that addresses both issues for the computation of several graph-theoretical similarity measures, including # common neighbors, and Personalized PageRank. We show how to tackle the imbalance in the graphs to speed up the computation and provide efficient real-time algorithms for computing rankings for an arbitrary subset of categories. Finally, we show experimentally the accuracy of our approach with real-world data, using both public graphs and a very large dataset from Google AdWords.
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Yield Optimization of Display Advertising with Ad Exchange
S. Muthukrishnan
ACM Conference on Electronic Commerce (2011)
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In light of the growing market of Ad Exchanges for the real-time sale of advertising slots, publishers face new challenges in choosing between the allocation of contract-based reservation ads and spot market ads. In this setting, the publisher should take into account the tradeoff between short-term revenue from an Ad Exchange and quality of allocating reservation ads. In this paper, we formalize this combined optimization problem as a stochastic control problem and derive an efficient policy for online ad allocation in settings with general joint distribution over placement quality and exchange bids. We prove asymptotic optimality of this policy in terms of any trade-off between quality of delivered reservation ads and revenue from the exchange, and provide a rigorous bound for its convergence rate to the optimal policy. We also give experimental results on data derived from real publisher inventory, showing that our policy can achieve any pareto-optimal point on the quality vs. revenue curve. Finally, we study a parametric training-based algorithm in which instead of learning the dual variables from a sample data (as is done in non-parametric training-based algorithms), we learn the parameters of the distribution and construct those dual variables from the learned parameter values. We compare parametric and non-parametric ways to estimate from data both analytically and experimentally in the special case without the ad exchange, and show that though both methods converge to the optimal policy as the sample size grows, our parametric method converges faster, and thus performs better on smaller samples.
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Online Stochastic Packing Applied to Display Ad Allocation
Monika Henzinger
ESA (1) (2010), pp. 182-194
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Inspired by online ad allocation, we study online stochastic packing integer programs from theoretical and practical standpoints. We first present a near-optimal online algorithm for a general class of packing integer programs which model various online resource allocation problems including online variants of routing, ad allocations, generalized assignment, and combinatorial auctions. As our main theoretical result, we prove that a simple dual training-based algorithm achieves a (1 − o(1))-approximation guarantee in the random order stochastic model. This is a significant improvement over logarithmic or constant-factor approximations for the adversarial variants of the same problems (e.g. factor 1−1e1−1e for online ad allocation, and log(m) for online routing). We then focus on the online display ad allocation problem and study the efficiency and fairness of various training-based and online allocation algorithms on data sets collected from real-life display ad allocation system. Our experimental evaluation confirms the effectiveness of training-based algorithms on real data sets, and also indicates an intrinsic trade-off between fairness and efficiency.
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Auctions with intermediaries: extended abstract
S. Muthukrishnan
Mallesh M. Pai
ACM Conference on Electronic Commerce (2010), pp. 23-32
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Inspired by online advertisement exchange systems, we study a setting where potential buyers of a unique, indivisible good attempt to purchase from a central seller via a set of intermediaries. Each intermediary has captive buyers, and runs an auction for a 'contingent' good. Based on the outcome, the intermediary bids in a subsequent upstream auction run by the seller. In this paper, we study the equilibria and incentives of intermediaries and the central seller.
We find that combining the notion of optimal auction design with the double-marginalization arising from the presence of intermediaries yields new strategic elements not present in either setting individually: we show that in equilibrium, revenue-maximizing intermediaries will use an auction with a randomized reserve price chosen from an interval. We characterize the interval and the probability distribution from which this reserve price is chosen as a function of the distribution of buyers' types. Furthermore, we characterize the revenue maximizing auction for the central seller by taking into account the effect of his choice of mechanism on the mechanisms offered by the intermediaries. We find that the optimal reserve price offered by the seller decreases with the number of buyers (but remains strictly positive); in contrast to the classical optimal auction without intermediaries, where the reserve price is independent of the number of buyers.
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Online Ad Assignment with Free Disposal
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S. Muthukrishnan
Workshop of Internet Economics (WINE) (2009), pp. 374-385
An Online Mechanism for Ad Slot Reservations with Cancellations
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Florin Constantin
S. Muthukrishnan
Fourth Workshop on Ad Auctions; Symposium on Discrete Algorithms (SODA) (2009)
Online Stochastic Matching: Beating 1-1/e
S. Muthukrishnan
Symposium on the Foundations of Computer Science (FOCS) (2009)
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We study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet. In the online, but adversarial case, the celebrated result of Karp, Vazirani and Vazirani gives an approximation ratio of 1−1e≃0.632, a very familiar bound that holds for many online problems; further, the bound is tight in this case. In the online, stochastic case when nodes are drawn repeatedly from a known distribution, the greedy algorithm matches this approximation ratio, but still, no algorithm is known that beats the 1−1e bound.Our main result is a 0.67-approximation online algorithm for stochastic bipartite matching, breaking this 1−1e barrier. Furthermore, we show that no online algorithm can produce a 1−ϵ approximation for an arbitrarily small ϵ for this problem. Our algorithms are based on computing an optimal offline solution to the expected instance, and using this solution as a guideline in the process of online allocation. We employ a novel application of the idea of the power of two choices from load balancing: we compute two disjoint solutions to the expected instance, and use both of them in the online algorithm in a prescribed preference order. To identify these two disjoint solutions, we solve a max flow problem in a boosted flow graph, and then carefully decompose this maximum flow to two edge-disjoint (near-) matchings. In addition to guiding the online decision making, these two offline solutions are used to characterize an upper bound for the optimum in any scenario. This is done by identifying a cut whose value we can bound under the arrival distribution. At the end, we discuss extensions of our results to more general bipartite allocations that are important in a display ad application.
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Algorithmic Methods for Sponsored Search Advertising
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S. Muthukrishnan
Performance Modeling and Engineering (Proc. SIGMETRICS 2008 Tutorial Sessions), Springer, pp. 91-124
