Mining of Business Data

Applying statistical tools to help understand business processes and make informed business decisions has attracted enormous amount of research interests in recent years. In this dissertation, we develop and apply data mining techniques to two sources of data, online bidding data for eBay and offline sales transaction data from a grocery product distributor. We mine online auction data to develop forecasting models and bidding strategies and mine offline sales transaction data to investigate sales people's price formation process. We start with discussing bidders' bidding strategies in online auctions. Conventional bidding strategies do not help bidders select an auction to bid on. We propose an automated and data-driven strategy which consists of a dynamic forecasting model for auction closing price and a bidding framework built around this model to determine the best auction to bid on and the best bid amount. One important component of our bidding strategy is a good forecasting model. We investigate forecasting alternatives in three ways. Firstly, we develop model selection strategies for online auctions (Chapter 3). Secondly, we propose a novel functional K-nearest neighbor (KNN) forecaster for real time forecasting of online auctions (Chapter 4). The forecaster uses information from other auctions and weighs their contribution by their relevance in terms of auction features. It improves the predictive performance compared to several competing models across various levels of data heterogeneity. Thirdly, we develop a Beta model (Chapter 5) for capturing auction price paths and find this model has advantageous forecasting capability. Apart from online transactions, we also employ data mining techniques to understand offline transactions where sales representatives (salesreps) serve as media to interact with customers and quote prices. We investigate the mental models for salesreps' decision making, and find that price recommendation makes salesreps concentrate on cost related information. In summary, the dissertation develops various data mining techniques for business data. Our study is of great importance for understanding auction price formation processes, forecasting auction outcomes, optimizing bidding strategies, and identifying key factors in sales people's decision making. Those techniques not only advance our understanding of business processes, but also help design business infrastructure

A Rule-driven Approach for Defining the Behavior of Negotiating Software Agents

One problem with existing agent-mediated negotiation systems is that they rely on ad hoc, static, non-adaptive, and hardcoded schemes to represent the behaviour of agents. This limitation is probably due to the complexity of the negotiation task itself. Indeed, while negotiating, software (human) agents face tough decisions. These decisions are based not only on the information made available by the negotiation server, but on the behaviour of the other participants in the negotiation process as well. The information and the behaviour in question are constantly changing and highly uncertain. In the first part of the paper, we propose a rule-driven approach to represent, manage and explore negotiation strategies and coordination information. For that, we divide the behaviour of negotiating agents into protocols, strategies and coordination. Among the many advantages of the proposed solution, we can cite the high level of abstraction, the closeness to human understanding, the versatility, and the possibility to modify the agents' behaviour during the negotiation process. To validate our solution, we ran many agent tournaments, and used the rule-driven approach to implement bidding strategies that are common in the English and Dutch auctions. We also implemented simple coordination schemes across several auctions. The ongoing validation work is detailed and discussed in the second part of the paper. Un des inconvénients qu'on retrouve fréquemment dans les systèmes de négociation par agents est qu'ils reposent sur des schémas ad-hoc, non adaptatifs et figés dans le code pour représenter le comportement des agents. Cette limitation est probablement due à la complexité de l'activité de négociation elle-même. En effet, au cours de la négociation, les agents logiciels (humains) ont des décisions difficiles à prendre. Ces décisions ne sont pas seulement basées sur l'information disponible sur le serveur de négociation, mais aussi sur le comportement des autres participants durant le processus de négociation. L'information et le comportement en question changent constamment et sont très incertains. Dans la première partie de l'article, nous proposons une approche à base de règles pour représenter, gérer et explorer les stratégies de négociation ainsi que l'information de coordination. Parmi les nombreux avantages de la solution proposée, on peut citer le haut niveau d'abstraction, la proximité avec la compréhension humaine, la souplesse d'utilisation et la possibilité de modifier le comportement des agents durant le processus de négociation. Pour valider notre solution, nous avons effectué plusieurs tournois entre agents et utilisé l'approche à base de règles pour implémenter des stratégies simples applicables à l'enchère anglaise et à l'enchère hollandaise. Nous avons aussi implémenté des schémas simples de coordination impliquant plusieurs enchères. Le travail de validation, en cours, est détaillé et discuté dans la seconde partie de l'article.e-negotiation, online auction, software agent, negotiation strategy, coordination, rule-based system, rule engine, Négociation électronique, enchères en ligne, agents logiciels, stratégie de négociation, coordination, système à base de règles, moteur de règles

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Does Resorting to Online Dispute Resolution Promote Agreements? Experimental Evidence

This paper presents an experiment performed to test the properties of an innovativebargaining mechanism (called automated negotiation) used to resolve disputes arising fromInternet-based transactions. The main result shows that the settlement rule tends to chillbargaining as it creates incentives for individuals to misrepresent their true valuations, whichimplies that automated negotiation is not able to promote agreements. However, this perverseeffect depends strongly on the conflict situation. When the threat that a disagreement occurs ismore credible, the strategic effect is reduced since defendants are more interested inmaximizing the efficiency of a settlement than their own expected profit. The implications ofthese results are then used to discuss the potential role of public regulation and reputationmechanisms in Cyberspace: Online Dispute Resolution, Electronic Commerce, Bargaining, Arbitration,Experimental Economics

A Proficient and Dynamic Bidding Agent for Online Auctions

E-consumers face biggest challenge of opting for the best bidding strategies for competing in an environment of multiple and simultaneous online auctions for same or similar items. It becomes very complicated for the bidders to make decisions of selecting which auction to participate in, place single or multiple bids, early or late bidding and how much to bid. In this paper, we present the design of an autonomous dynamic bidding agent (ADBA) that makes these decisions on behalf of the buyers according to their bidding behaviors. The agent develops a comprehensive method for initial price prediction and an integrated model for bid forecasting. The initial price prediction method selects an auction to participate in and then predicts its closing price (initial price). Then the bid forecasting model forecasts the bid amount by designing different bidding strategies followed by the late bidders. The experimental results demonstrated improved initial price prediction outcomes by proposing a clustering based approach. Also, the results show the proficiency of the bidding strategies amongst the late bidders with desire for bargai

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Optimal pricing using online auction experiments: A P\'olya tree approach

We show how a retailer can estimate the optimal price of a new product using observed transaction prices from online second-price auction experiments. For this purpose we propose a Bayesian P\'olya tree approach which, given the limited nature of the data, requires a specially tailored implementation. Avoiding the need for a priori parametric assumptions, the P\'olya tree approach allows for flexible inference of the valuation distribution, leading to more robust estimation of optimal price than competing parametric approaches. In collaboration with an online jewelry retailer, we illustrate how our methodology can be combined with managerial prior knowledge to estimate the profit maximizing price of a new jewelry product.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS503 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org