Dinesh Garg

CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices

Mobile-enabled social networks applications are becoming increasingly popular. Most of the current social network applications have been designed for high-end mobile devices, and they rely upon features such as GPS, capabilities of the world wide web, and rich media support. However, a significant fraction of mobile user base, especially in the developing world, own low-end devices that are only capable of voice and short text messages (SMS). In this context, a natural question is whether one can design meaningful social network-based applications that can work well with these simple devices, and if so, what the real challenges are. Towards answering these questions, this paper presents a social network-based recommender system that has been explicitly designed to work even with devices that just support phone calls and SMS. Our design of the social network based recommender system incorporates three features that complement each other to derive highly targeted ads. First, we analyze information such as customers address books to estimate the level of social affinity among various users. This social affinity information is used to identify the recommendations to be sent to an individual user. Second, we combine the social affinity information with the spatio-temporal context of users and historical responses of the user to further refine the set of recommendations and to decide when a recommendation would be sent. Third, social affinity computation and spatio-temporal contextual association are continuously tuned through user feedback. We outline the challenges in building such a system, and outline approaches to deal with such challenges.

Design of six sigma supply chains

Variability reduction and business-process synchronization are acknowledged as keys to achieving sharp and timely deliveries in supply-chain networks. In this paper, we introduce a new notion, which we call six sigma supply chains to describe and quantify supply chains with sharp and timely deliveries, and develop an innovative approach for designing such networks. The approach developed in this paper is founded on an intriguing connection between mechanical design tolerancing and supply-chain lead-time compression. We show that the design of six sigma supply chains can be formulated as a mathematical programming problem, opening up a rich new framework for studying supply-chain design optimization problems. To show the efficacy of the notion and the design methodology, we focus on a design optimization problem, which we call the inventory optimization (IOPT) problem. Given a multistage supply-chain network, the IOPT problem seeks to find optimal allocation of lead time variabilities and inventories to individual stages, so as to achieve required levels of delivery performance in a cost-effective way. We formulate and solve the IOPT problem for a four-stage make-to-order liquid petroleum gas supply chain. The solution of the problem offers rich insights into inventory-service level tradeoffs in supply-chain networks and proves the potential of the new approach presented in this paper. Note to Practitioners-This paper builds a bridge between mechanical design tolerancing and supply-chain management. In particular, the paper explores the use of statistical tolerancing techniques in achieving outstanding delivery performance through variability reduction. Informally, a six sigma supply chain is that which delivers products within a customer specified delivery window, with at most 3.4 missed deliveries per million. The innovations in this paper are the following: 1) to define two performance metrics delivery probability and delivery sharpness to describe the precision and accuracy of deliveries, in terms of process capability indexes C/sub p/,C/sub pk/, and C/sub pm/; 2) to formulate the supply-chain design optimization problem using the process capability indices; 3) to suggest an efficient solution procedure for the design optimization problem. The paper presents the case study of a two-echelon distribution network and using the framework developed in the paper shows the role of inventory in controlling lead time variability and achieving six sigma levels of delivery performance.

Achieving sharp deliveries in supply chains through variance pool allocation

Variability reduction and business process synchronization are acknowledged as key to achieving sharp and timely deliveries in supply chain networks. In this paper, we develop an approach that facilitates variability reduction and business process synchronization for supply chains in a cost effective way. The approach developed is founded on an analogy between mechanical design tolerancing and supply chain lead time compression. We first present a motivating example to describe this analogy. Next, we define, using process capability indices, a new index of delivery performance called delivery sharpness which, when used with the classical performance index delivery probability, measures the accuracy as well as the precision with which products are delivered to the customers. Following this, we solve the following specific problem: how do we compute the allowable variability in lead time for individual stages of the supply chain so that specified levels of delivery sharpness and delivery probability are achieved in a cost-effective way? We call this the variance pool allocation (VPA) problem. We suggest an efficient heuristic approach for solving the VPA problem and also show that a variety of important supply chain design problems can be posed as instances of the VPA problem. One such problem, which is addressed in this paper, is the supply chain partner selection problem. We formulate and solve the VPA problem for a plastics industry supply chain and demonstrate how the solution can be used to choose the best mix of supply chain partners.

An Optimal Mechanism for Sponsored Search Auctions on the Web and Comparison With Other Mechanisms

In this paper, we first describe a framework to model the sponsored search auction on the Web as a mechanism design problem. Using this framework, we describe two well-known mechanisms for sponsored search auction - generalized second price (GSP) and Vickrey-Clarke-Groves (VCG). We then derive a new mechanism for sponsored search auction which we call optimal (OPT) mechanism. The OPT mechanism maximizes the search engines expected revenue, while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We then undertake a detailed comparative study of the mechanisms GSP, VCG, and OPT. We compute and compare the expected revenue earned by the search engine under the three mechanisms when the advertisers are symmetric and some special conditions are satisfied. We also compare the three mechanisms in terms of incentive compatibility, individual rationality, and computational complexity.

Price of anarchy of network routing games with incomplete information

We consider a class of networks where n agents need to send their traffic from a given source to a given destination over m identical, non-intersecting, and parallel links. For such networks, our interest is in computing the worst case loss in social welfare when a distributed routing scheme is used instead of a centralized one. For this, we use a noncooperative game model with price of anarchy as the index of comparison. Previous work in this area makes the complete information assumption, that is, every agent knows deterministically the amount of traffic injected by every other agent. Our work relaxes this by assuming that the amount of traffic each agent wishes to send is known to the agent itself but not to the rest of the agents; each agent has a belief about the traffic loads of all other agents, expressed in terms of a probability distribution. In this paper, we first set up a model for such network situations; the model is a noncooperative Bayesian game with incomplete information. We study the resulting games using the solution concept of Bayesian Nash equilibrium and a representation called the type agent representation. We derive an upper bound on price of anarchy for these games, assuming the total expected delay experienced by all the agents as the social cost. It turns out that these bounds are independent of the belief probability distributions of the agents. This fact, in particular, implies that the same bounds must hold for the complete information case, which is vindicated by the existing results in the literature for complete information routing games.

Mechanism design for time critical and cost critical task execution via crowdsourcing

An exciting application of crowdsourcing is to use social networks in complex task execution. In this paper, we address the problem of a planner who needs to incentivize agents within a network in order to seek their help in executing an atomic task as well as in recruiting other agents to execute the task. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planners goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We identify a set of desirable properties that should ideally be satisfied by a crowdsourcing mechanism. In particular, sybil-proofness and collapse-proofness are two complementary properties in our desiderata. We prove that no mechanism can satisfy all the desirable properties simultaneously. This leads us naturally to explore approximate versions of the critical properties. We focus our attention on approximate sybil-proofness and our exploration leads to a parametrized family of payment mechanisms which satisfy collapse-proofness. We characterize the approximate versions of the desirable properties in cost critical and time critical domain.

A groves mechanism approach to decentralized design of supply chains

In this paper, a generic optimization problem arising in supply chain design is modeled in a game theoretic framework and solved as a decentralized problem using a mechanism design approach. We show that the entities in a supply chain network can be naturally modeled as selfish, rational, and intelligent agents interested in maximizing certain payoffs. This enables us to define a supply chain design game and we show that the well known Groves mechanisms can be used to solve the underlying design optimization problem. We illustrate our approach with a representative three stage distribution process of a typical automotive supply chain.

Adaptive policies for selecting groupon style chunked reward ads in a stochastic knapsack framework

Stochastic knapsack problems deal with selecting items with potentially random sizes and rewards so as to maximize the total reward while satisfying certain capacity constraints. A novel variant of this problem, where items are worthless unless collected in bundles, is introduced here. This setup is similar to the Groupon model, where a deal is off unless a minimum number of users sign up for it. Since the optimal algorithm to solve this problem is not practical, several adaptive greedy approaches with reasonable time and memory requirements are studied in detail - theoretically, as well as, experimentally. Worst case performance guarantees are provided for some of these greedy algorithms, while results of experimental evaluation demonstrate that they are much closer to optimal than what the theoretical bounds suggest. Applications include optimizing for online advertising pricing models where advertisers pay only when certain goals, in terms of clicks or conversions, are met. We perform extensive experiments for the situation where there are between two and five ads. For typical ad conversion rates, the greedy policy of selecting items having the highest individual expected reward obtains a value within 5% of optimal over 95% of the time for a wide selection of parameters.

A Nash bargaining approach to retention enhancing bid optimization in sponsored search auctions with discrete bids

Bid optimization is now becoming quite popular in sponsored search auctions on the Web. Given a keyword and the maximum willingness to pay of each advertiser interested in the keyword, the bid optimizer generates a profile of bids for the advertisers with the objective of maximizing customer retention without compromising the revenue of the search engine. In this paper, we present a bid optimization algorithm that is based on a Nash bargaining model where the first player is the search engine and the second player is a virtual agent representing all the bidders. We make the realistic assumption that each bidder specifies a maximum willingness to pay values and a discrete, finite set of bid values. We show that the Nash bargaining solution for this problem always lies on a certain edge of the convex hull such that one end point of the edge is the vector of maximum willingness to pay of all the bidders. We show that the other endpoint of this edge can be computed as a solution of a linear programming problem. We also show how the solution can be transformed to a bid profile of the advertisers.

Mechanism Design for Single Leader Stackelberg Problems and Application to Procurement Auction Design

In this paper, we focus on mechanism design for single leader Stackelberg problems, which are a special case of hierarchical decision making problems in which a distinguished agent, known as the leader, makes the first move and this action is followed by the actions of the remaining agents, which are known as the followers. These problems are also known as single leader rest follower (SLRF) problems. There are many examples of such problems in the areas of electronic commerce, supply chain management, manufacturing systems, distributed computing, transportation networks, and multiagent systems. The game induced among the agents for these problems is a Bayesian Stackelberg game, which is more general than a Bayesian game. For this reason, classical mechanism design, which is based on Bayesian games, cannot be applied as is for solving SLRF mechanism design problems. In this paper, we extend classical mechanism design theory to the specific setting of SLRF problems. As a significant application of the theory developed, we explore two examples from the domain of electronic commerce-first-price and second-price electronic procurement auctions with reserve prices. Using an SLRF model for these auctions, we derive certain key results using the SLRF mechanism design framework developed in this paper. The theory developed has many promising applications in modeling and solving emerging game theoretic problems in engineering.