SCHEDULING ONLINE ADVERTISEMENTS USING INFORMATION RETRIEVAL AND NEURAL NETWORK/GENETIC ALGORITHM BASED METAHEURISTICS

Transcription

1 SCHEDULING ONLINE ADVERTISEMENTS USING INFORMATION RETRIEVAL AND NEURAL NETWORK/GENETIC ALGORITHM BASED METAHEURISTICS By JASON DEANE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

2 Copyright 2006 by Jason Deane

3 This document is dedicated to my entire family, but especially to Amanda, Conner, mom and pawpaw. Amanda and Conner provided me with the necessary strength, determination and never ending support and were my inspiration in pursuing and finishing my PhD. Mom and pawpaw are, without a doubt, the two most influential people in my life. For good and for bad, everything that I am is as a result of my never ending effort to model myself after these two amazing people. Pawpaw was the kindest and most sincere person that I have ever met and although he s in a better place now, I still think of him every day. My mother is the strongest and hardest working person that I know and without her many sacrifices, my life could have been completely different and I would never have had the opportunity to achieve this goal. Thank you!

4 ACKNOWLEDGMENTS I would like to especially thank my wife Amanda for supporting and putting up with me throughout this process. I know that it was not easy. I would also like to thank our families for their never ending support throughout this very challenging endeavor. In addition, I would like to thank my dissertation committee and the DIS department staff for their support and guidance. In particular I would like to thank and acknowledge my advisor, Anurag Agarwal, and my co-chair, Praveen Pathak, for their countless hours of training and support. I couldn t have done it without you!! iv

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS... iv LIST OF TABLES... vii LIST OF FIGURES... ix ABSTRACT...x CHAPTER 1 INTRODUCTION AND MOTIVATION ONLINE ADVERTISING Definitions and Pricing Models Literature Review INFORMATION RETRIEVAL METHODOLOGIES Overview Data Pre-processing Vector Space Model Structural Representation WordNet LARGE SCALE SEARCH METHODOLOGIES Overview Genetic Algorithms Neural Networks The No Free Lunch Theorem RESEARCH MODEL(S) Problem Summary Information Retrieval Based Ad Targeting Online Advertisement Scheduling The Modified Maxspace Problem (MMS) The Modified Maxspace Problem with Ad Targeting (MMSwAT)...68 v

6 5.3.3 The Modified Maxspace Problem with Non-Linear Pricing (MMSwNLP) Model Solution Approaches Augmented Neural Network (AugNN) Genetic Algorithm (GA) Hybrid Technique Parameter Selection Problem set Development RESULTS Information Retrieval Based Ad Targeting Results Discussion of the Information Retrieval Based Ad Targeting Results Online Advertisement Scheduling Results Modified Maxspace (MMS) Problem Result The Modified Maxspace with Ad Targeting (MMSwAT) Problem Results The Modified Maxspace wint Nonlinear Pricing (MMSwNLP) Problem Results Discussion of the Online Advertisement Scheduling Results SUMMARY, CONCLUSIONS AND FUTURE RESEARCH APPENDIX A B C GA AND AUGNN PARAMETER AND SETTING DEFINITIONS LIST OF ADVERTISED PRODUCTS AND SERVICES AND THEIR RESPECTIVE CHARACTERISTIC ARRAYS SAMPLE DOCUMENTS FOR ONE USER FROM THE IR BASED AD TARGETING PROCESS LIST OF REFERENCES BIOGRAPHICAL SKETCH vi

7 LIST OF TABLES Table page 1 Structural Element Weighting Schemes AugNN Parameter Values GA Parameter Values Hybrid Parameter Values Summary of Mean Student Rankings for the 4 Selection Methods T Test-Scheme 1 & Random Selection T Test-Scheme 2 & Random Selection T Test-Scheme 3 & Random Selection Summary of Mean Student Rankings for the Three Weighting Schemes T Test-Scheme 1-5 & Scheme T Test-Scheme 1-5 & Scheme T Test-Scheme 1-5 & Scheme T Test-Scheme 1-5 & Scheme T Test-Scheme 2-5 & Scheme T Test-Scheme 2-5 & Scheme T Test-Scheme 2-5 & Scheme T Test-Scheme 2-5 & Scheme T Test-Scheme 3-5 & Scheme T Test-Scheme 3-5 & Scheme T Test-Scheme 3-5 & Scheme vii

8 21 T Test-Scheme 3-5 & Scheme T Test-Scheme 1 & Scheme T Test-Scheme 1 & Scheme T Test-Scheme 2 & Scheme Problem Results MMSwAT Comparison of Results MMSwNLP Comparison of Results viii

9 LIST OF FIGURES Figure page 1 A Screen Print of Yahoo s Shopping Page. Notice the advertising banner down the right hand side of the Web page Pictorial Representation of Information Flow in Traditional Print Advertising Pictorial Representation of the Information Flow in Online Advertising Geometric Representation of the VSM Classes of Search Methods (Basic Model Borrowed from [54]) Pictorial Representation of the Cerebral Cortex [91] Pictorial Representation of a Basic Feed Forward ANN [91] Selected Parents Prior to Crossover Resulting Offspring Child 2 Prior to Mutation Child 2 After Mutation Q-Q Plot of Student Response Values...87 ix

10 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy SCHEDULING ONLINE ADVERTISEMENTS USING INFORMATION RETRIEVAL AND NEURAL NETWORK/GENETIC ALGORITHM BASED METAHEURISTICS By Jason Deane August, 2006 Chair: Anurag Agarwal Cochair: Praveen Pathak Major Department: Decision and Information Sciences As a result of the recent technological proliferation, online advertising has become a very powerful and popular method of marketing; industry revenue is growing at a record pace. One very challenging problem which is faced by those on the publishing side of the industry is ad targeting. In an attempt to maximize revenue, publishers try their best to expose web surfers to a set of advertisements which are closely aligned with their interests and needs. In this work, we present and test an information retrieval based ad targeting technique which shows promise as an alternative solution method for this problem. A second, very difficult, challenge faced by online ad publishers is the development of an ad schedule which makes the most efficient use of their available advertisement space. We introduce three versions of this very difficult problem and test several potential solution techniques for each of them. x

11 CHAPTER 1 INTRODUCTION AND MOTIVATION Despite residual fears from the dot-com decline of 2000, many seem to be once again embracing the Web. Worldwide Internet usage is at an all time high, broadband access is soaring and many households are turning away from their televisions in lieu of their computer screens [1]. The proliferation of the fiber optic telecommunication infrastructure which was left over from the telecom boom of the 1990 s has made broadband connectivity accessible and affordable for almost any family. As a result, the online experience has been vastly improved and is extremely popular with the technology generation. According to Tom Hyland, Partner and New Media Group Chair, PricewaterhouseCoopers, this has created a mass audience of Internet users which simply cannot be ignored by advertisers. Corporate America is beginning to realize the potential importance of expanding its advertising portfolio to include the online channel. This sentiment is echoed by many corporate executives. David Garrity, a financial analyst at Caris & Company, a Wall Street investment firm, asserts that "Every indication is that corporate advertising budgets are increasingly allocated to the Internet" [2, p.1]. Ty Montague, Wieden & Kennedy's chief creative officer, believes "Whereas people are zapping most TV advertising, the Net is amazing for drawing people in, if our ingenuity is up to it" [3, p.1]. These comments are typical of the current claims about the growth of the online advertising channel. The recent trend in online advertisement spending fully supports these claims. According to a recent Price Waterhouse Coopers report, industry revenue for the calendar 1

12 2 year 2005 totaled $12.5 billion which represents a 23% year over year increase in comparison with 2004 results [4]. Industry wide revenue has increased in 12 of the last 13 quarters. In addition, future projections of widespread mobile Internet access demand are expected to provide an additional revenue boost for the industry. It is estimated that online advertisement revenues for the US alone will grow to $18.9 billion by 2010 [4]. Motivated by this upward sloping trend in Internet advertising demand, many companies (e.g., Google, Yahoo, AOL, etc.) have adopted a business model which is heavily dependent upon the revenue stream generated from their publishing of online advertisements [5]. As a result, efforts to improve the online advertisement scheduling process are under extreme demand. In personal conversations with Doron Welsey, Director of Industry Research for the Interactive Advertising Bureau (IAB), and Rick Bruner, research analyst for DoubleClick, a leading online advertising agency, both indicated that they have been inundated with companies seeking help with their online advertising efforts. They also indicated that research which helps overcome the IT-related challenges that currently face the industry is critically needed and therefore is likely to be important to industry experts and academicians alike. In this dissertation, we apply information retrieval and artificial intelligence methodologies in an attempt to provide efficient, appealing solution alternatives to one of the most difficult and compelling problems facing publishers, online banner advertisement scheduling. Given the popularity of banner advertising and the considerable revenue which it generates, even a small improvement in the efficiency and/or quality of the scheduling process could result in a considerable increase in revenue.

13 3 The goals of this thesis are three fold. First, we propose a methodology which, based on a user s recent Web surfing behavior, provides an estimate of his or her level of interest in a particular advertisement. Second, we introduce three new real-world variations of the strongly NP-hard online advertisement scheduling optimization problem. Finally, we develop and test several heuristic and meta-heuristic solution algorithms for each of the new models that we propose. Information Retrieval (IR) is an area of research which attempts to extract usable information from textual data. We propose a method by which Information retrieval and ontological methodologies are utilized to exploit a user s recent Web surfing history in an effort to categorize ads based on the user s predicted level of interest. IR has historically been employed in the field of library sciences, but it has recently gained favor in many other fields including Internet search and cyber security. The power of IR is its ability to handle textual information. Information retrieval has been applied in many domains, including document sorting, document retrieval, inference development and query response. We use IR techniques to leverage the textual representation of a user s html Web surfing history in the creation of a weighted characteristic array for each user. We create similar arrays for each advertisement and use several similarity measures to strategically create a schedule of user-advertisement assignments. The basic online advertisement scheduling optimization problem has been addressed in the literature. Because it is an NP-hard problem [6], most of the variations have been limited to linear pricing models which seek to maximize the number of ads served or the number of times an ad is clicked. We introduce several new model

14 4 variations designed to address realistic issues such as nonlinear pricing and advertisement targeting. Obviously, the NP-hard nature of the basic linear problem means that these variations will be even more difficult to solve optimally. We develop and test several heuristic algorithms which may allow efficient generation of near-optimal solutions for these models. Machine learning (ML) is the study of computer algorithms that improve automatically through experience [7]. Machine learning is a subset of artificial intelligence which has received considerable attention due to the recent increase in available computing power. Machine learning methods such as decision trees, logit functions, and neural networks have been applied successfully to a wide array of problems, including optimization problems, and have therefore proven to be valuable tools in the development of heuristic solution approaches. We combine neural network and genetic algorithm techniques with several base heuristics in an effort to provide efficient robust solution techniques for multiple variations of the online advertisement scheduling problem.

15 CHAPTER 2 ONLINE ADVERTISING This chapter presents a general overview of the online advertising industry and the associated research. In section 2.1, we provide a review of the basic definitions and pricing models. In section 2.2, we provide a review of the online advertisement scheduling literature. 2.1 Definitions and Pricing Models There are three primary participants in online advertising. At the top of the chain is the advertiser. This is a company that enters into an agreement with a publisher in order to enlist the publisher s assistance in the serving of their online advertisements. More times than not, the ads are delivered to users of the publisher s Web pages. The publisher is a company that expends resources in an effort to publish online advertisements in an effort to generate revenue. The customer is the individual who browses Web pages and may or may not respond to an ad in a manner that is verifiable, such as clicking the ad. Publishers could be paid by the advertisers for their service according to a number of possible schemes. The first category of pricing models is often referred to as Impression Based Pricing Models because the publisher is paid entirely on the basis of serving the ad, which is called an impression on the Webpage, and not on any action taken by the customer. Thus, the publisher is paid whether or not the customer shows any interest in the ad. The most basic impression based model is CPM Linear Pricing. CPM is short for cost per mille (mille is Latin for 1,000). In this scheme, the publisher is paid a fixed fee for each 1,000 ads that are served. The fee is based on the size of the ad 5

16 6 and increases in a linear fashion. In addition, the rate may be different depending on the chosen Web pages (sports, news, etc.), the time of day, etc.; however, many publishers price each slot identically in an effort to simplify the accounting and scheduling operations. Larger ads decrease the publishers flexibility to schedule ads within a fixed banner area; therefore, publishers might expect a premium for larger ads. CPM Nonlinear Pricing allows for this expectation. It is the same as the CPM Linear Pricing except that the pricing function with respect to advertisement size is either a concave or a step function instead of a linear function. The third type of CPM model is called Modified CPM. This is a model which is being used by publishers in an effort to increase the revenue which they receive for the advertising space on their generic/non-targeted Web pages. Advertising space for the targeted pages such as sports, automotive, and real estate is in high demand; however, the space on the other non-targeted pages is much harder to sell. As a result, publishers have started trying to charge a premium for the advertising space on these non-targeted pages by employing consumer classification. The basic idea is that a user is classified based on his or her click behavior and then served ads based on this classification. As an example, a user that visits the sports page more than some threshold number of times is classified as a sports person. The publisher then targets this consumer when he or she visits one of the non-targeted pages and serves the consumer a sports related ad. The revenue that the publisher is able to demand in this situation is not as high as it would have been had he or she served the ad on the sports page, but it is higher than he or she could have received for another random ad placement on the non-targeted page. Under all of the CPM based models, the advertiser bears all of the financial risk. This is because the advertiser must

17 7 pay the publisher the agreed upon rate regardless of how well the advertisement campaign performs. The other primary category of pricing models, in contrast to impression-based models, is Performance Based Models. These are models within which the publisher is paid based solely on some pre-defined measure of ad campaign performance. Performance Based CPC (Cost Per Click) is a scheme in which the publisher is paid a fee each time the advertiser s ad is clicked. Performance Based CPS (Cost Per Sale) is where the publisher is paid a fee each time one of the served ads results in a sale. In Performance Based CPR (Cost per Registration), the publisher is paid a fee each time a consumer sets up an account with the advertiser as a result of the advertisement. Under all of the performance based models, the publisher bears all of the financial risk. This is because the publisher is paid nothing for simply publishing the advertisements. Instead, he or she is paid only if pre-defined performance criteria are met. Finally, Hybrid Pricing Models are pricing models which combine two or more of the above models. Often, this type of model will include the CPM and one or more of the performance based models in an effort to establish an equitable risk sharing situation between the publisher and the advertiser. These have become very popular in industry. 2.2 Literature Review The process of scheduling online advertisements can be a very challenging and dynamic task which is characterized by a wide array of obstacles and constraints. The set of constraints and difficulties differs vastly from publisher to publisher, depending on their effort level and the methods with which they choose to address the problem. Factors which affect the relative complexity level of the problem include which pricing models the publisher chooses; which, if any, targeting efforts the publisher attempts to

18 8 employ, and which additional artificial intelligence techniques the publisher chooses to build into their scheduling algorithms. Thus far, the primary focus of academic researchers has been on addressing the most basic of these situations: a CPM pricing model with no applied intelligence or targeting. From a publisher s perspective, advertising space is a precious non-renewable resource which, if used efficiently, can drive both current revenues and future demand. The two primary goals, from an advertisement scheduling perspective, are to minimize the amount of unused advertising space and to maximize the probability that a customer will have interest in the advertisements which he or she is served. Depending on the agreed upon pricing model, these goals can take on different levels of importance with respect to the maximization of revenue. Publishers are currently compensated based on some combination of the amount of Web space and number of advertisement impressions delivered for an advertiser, and/or their score on a pre-defined set of performance based measures such as the number of clicks, number of leads, or the number of sales. The original pricing model for the online advertising industry was the CPM model. The CPM model is a basic pricing structure which was adopted from traditional print advertising, within which the publisher is compensated an agreed upon rate for every thousand advertisement impressions that they deliver. This model was very popular in the 1990 s and is still being used by many companies [8]. Thus far academic research literature has been primarily focused on models which are based on this pricing strategy. The seminal online advertisement scheduling paper by Adler, Gibbons and Matias [6] introduced two basic problems, the Minspace and the Maxspace problem, and proved that both are NP-hard in the strong sense. The Maxspace problem is formulated based on

19 9 the CPM pricing model. The objective of the Maxspace problem is to find a feasible schedule of ads such that the total occupied slot space is maximized given that the slots have a fixed capacity and the ads are of differing sizes and differing display frequencies. There are several assumptions which are inherent in the formulation of these two models. First, it is assumed that each banner/time slot is the same size, S. Second, it is assumed that all of the ads have the same width, which is equal to the width of the banner. This is common practice when the banner ad space is found on either side of a Web page. See figure 1 for an example from Yahoo s shopping page. Next, it is assumed that each ad has a height which is less than or equal to the height of the banner. It is also assumed that any user who accesses the Web site during a given time slot will see the same set of advertisements. In addition, the authors assume that there is a positive linear relationship between advertisement size and the revenue which is generated. Therefore, the objective is to find a feasible set of ads which maximizes the used advertising space. An IP formulation of the Maxspace problem is as follows: Max j= 1 i= 1 st. Constraints (1) sx S, j= 1, 2,.., N i = 1 (2) x = w y, i = 1, 2,..., n (3) (4) N n n N j = 1 x y ij i s x i i ij ij ij i i 1 if Ad i is assigned to ad slot j = 0 otherwise 1 if ad i is assigned = 0 otherwise

20 10 Where : n - total number of advertisements available for scheduling over the planning period N - total number of available time slots in the planning period S - Banner height s - height of advertisement i, i = 1, 2,..., n i w - display frequency of advertisements i, i = 1,2,..., n i Figure 1. A Screen Print of Yahoo s Shopping Page. Notice the advertising banner down the right hand side of the Web page.

21 11 Constraint (1) ensures that the combined height of the set of ads which are scheduled for each banner slot does not exceed the available space. Another assumption of the model is that if an advertisement is chosen, the number of delivered impressions for that ad must exactly equal its pre-defined frequency, w i. Constraints (2 & 4) combine to ensure that this relationship is guaranteed. Constraint (3) ensures that at most one copy of each ad can appear in any given slot. In other words, it is not acceptable to schedule the same ad multiple times in a given banner slot. This constraint represents a very important aspect of the online advertisement scheduling problem which distinguishes it from other related bin packing and scheduling problems. The Minspace problem is very similar to the Maxspace problem. However, there are a couple of significant differences. An IP formulation of the Minspace problem is as follows: M in S st. Constraints (1) sx S, j= 1, 2,.., N i i = 1 (2) x = w y, i = 1, 2,..., n (3) x (4) y Where: n N j = 1 ij i S = size of the slots i i ij ij i i 1 if Ad i is assigned to ad slot j = 0 otherwise 1 if ad i is assigned = 0 otherwise s = size of ad i, i N w = frequency of ad i

22 12 One primary difference is that this problem does not assume that the size of the banner/time slot is fixed. Instead, the objective of this problem is to schedule all of the ads while minimizing the height of the tallest slot. The authors postulate that this problem may be useful during the Website design phase. For this problem, they developed the Largest Size Least Full (LSLF) algorithm, which is a 2-approximation and they developed a Subset-LSLF algorithm for the Maxspace problem. The LSLF algorithm, which can be implemented in timeo( w + sort( A)), is a basic greedy heuristic. The steps are detailed below. Largest Size Least Full (LSLF) Algorithm n i= 1 Sort the ads in descending order of size Assign each of the ads in sorted order, ad i is assigned to the w i least full slots. The Subset-LSLF algorithm is very similar. The steps are as follows. Subset Largest Size Least Full Algorithm Classify the ads into two subsets based on their relative size. If s i = S, the ad is placed in subset B s otherwise, it is placed in subset B k. Calculate the volume of advertisements for each subset = sw i i Choose the subset with the largest volume. Assign the ads from this subset as long as there is sufficient space available. For the B k subset, use the LSLF algorithm for placement. The authors show that this is a 2-approximation algorithm for the special case where ad widths are divisible and the profit of each ad is proportional to its volume (width times display frequency). One limitation of their work is that nearly all of their meaningful results pertain to this special case where the ad sizes are divisible. Dawande, Kumar and Sriskandarajah [5] propose additional heuristic solution techniques for both the Maxspace and the Minspace problems. For the Minspace problem, they suggest a linear programming relaxation (LPR) based algorithm and a i

23 13 Largest Frequency Least Full (LFLF) heuristic. The authors prove that the LPR is a 2- approximation algorithm and that this bound is asymptotically tight. In addition, they prove that the integrality gap for the algorithm is bounded above by s max and that the time complexity is 3 3 OnNL ( + Nn ( + N 1) where L is the length of the binary encoding of LP min. The LFLF heuristic is very similar to the LSLF heuristic designed by Adler et al. [6] except that the ads are assigned in the non-increasing order of their frequency instead of non-increasing order of their size. The time complexity of the algorithm is On ( logn+ nnlog N) which is comparable to that of LSLF; however, the performance bounds are slightly better. The performance bound for the LFLF algorithm for the Minspace problem is: * f (solution of LFLF) / f (optimum solution val of IP) rwhere 0 1 n * = si i= 1 ( ) 1. r= 2 1/( N w0 + 1) whenw0 N + 1 / 2 * * 2. r = max{1,(2 N /( N + 2))(( S s0) / S } whenw0 > ( N + 1) / 2 w0 = min{ w1,..., wn} s = min{ s,..., s } S n The bound is tight in both cases. The authors also introduce two heuristics, MAX1 and MAX2, for the Maxspace problem. These algorithms involve a decomposition of the set of ads into two subsets based on their frequencies. Based on the total weight of ads in each subset, the algorithm gives priority to one of the subsets. All of the ads from that subset are assigned using the LSLF heuristic and then the other subset of ads is assigned likewise. Max1 has a time complexity of On ( log n+ nnlog N) and a performance bound * f / f 1/4 1/4N +. Max2, which is a little more complicated, has a time complexity of 3 OnN ( + nnlog N) and a performance bound * f / f 3/10. The authors tested their

24 14 heuristics against a test bed of problems. They created 10 sets of problems, with each set containing 10 problems. The number of slots, N, ranged from [25, 100] and the ad sizes ranged from [S/3, 2S/3]. One important contribution from their work is that they remove the restrictive limitations on advertisement size which were present in the work by Adler et al. [6]. The average percentage gap between the heuristic and optimum solutions for the LFLF, Max1 and Max2 heuristics were approximately 30%, 15% and 20% respectively. Freund and Naor [9] propose additional heuristic-based solution techniques for the Maxspace and the Minspace problems. Following the trend set by Dawande et al. [5], this work also allows arbitrary ad sizes, but maintains all of the other assumptions originally set out by Adler et al. For the Minspace problem, they propose the Smallest Size Least Full (SSLF) heuristic. Their method is very similar to that of Adler et al.; however, their heuristic considers the ads for placement iteratively in non-decreasing order of size which is the exact opposite of the procedure proposed by Adler et al. For the Maxspace problem, they propose a (3+E) approximation algorithm which combines a knapsack relaxation and the SSLF heuristic. In addition, they also provide solution techniques for two special cases; ad widths not exceeding one half of the display area, and each advertisement must either occupy the entire area or else have a width at most one half of the display area. No test results are provided for any of the proposed algorithms. Menon and Amiri [10] propose and test Lagrangean relaxation and column generation solution techniques for a variation of the Maxspace problem. One major difference in their work is that they relax the advertisement frequency constraint. Instead

25 15 of requiring each ad to appear a pre-defined number of times, they set an upper bound for the number of times that each ad can appear. In their explanation, the authors make a concerted effort to distinguish the scheduling horizon from the planning horizon. The scheduling horizon corresponds to the length of time within which the publisher commits to deliver a set number of advertisement impressions for their consumers, where as the planning horizon is the period of time for which we are trying to schedule a set of ads to fill the banner space. They claim that the planning horizon should be shorter than the scheduling horizon in order to provide scheduling flexibility for the publisher. According to the authors, if these horizons are of unequal length as they recommend, the proposed upper bound on the ad frequency should correspond with the number of ad impressions left to be delivered for a given advertiser during the scheduling horizon. For example, assume that our planning horizon is one week and that the problem at hand is to develop an advertisement schedule for the third week of September. Let us also assume that we have promised Dell that we will deliver 1000 impressions of their ad during the month of September and thus far we have only delivered 100. In this situation, the upper bound for the number of times that Dell s ad could be scheduled during the planning horizon (the third week of September) is 900; however, there is no minimum requirement. The proposed relaxation definitely provides additional flexibility and in doing so simplifies the complexity of the problem considerably. However, it also creates another set of potential problems. In the hypothetical example above, with the model formulation provided by the authors, there is no way to guarantee that Dell s 1000 impressions would be delivered within the agreed upon time frame. For obvious reasons, this is probably not a desirable situation. The authors make a very compelling argument for their version of

26 16 the Maxspace problem, and it is likely that there are business situations within which their model would be extremely useful. However, the discussed limitation should be carefully considered prior to its application. To test their heuristics, the authors created a large data set which consisted of 1500 problems. The number of advertisements and the number of time slots ranged from [20, 200], and the size of the banner ranged from [40, 100]. In addition, the authors varied the selection process of the w i values, choosing from several different uniform distributions. They applied the column generation and the Lagrangean procedures to the entire data set. In addition, they combined the column generation procedure with a greedy based preprocessing heuristic and tested it against the entire data set. Their testing sequence indicated that the column generation procedure performs much better than the lagrangean relaxation procedure against their data set and that the initialization heuristic only enhanced column generation procedure s dominance. Dawande, Kumar and Sriskandarajah [11] propose three improved heuristic solution techniques for the Minspace problem. These solutions represent slightly better performance bounds than those presented in their earlier work. They introduce algorithms Min1 and Min2 which are both slight adaptations of their linear programming relaxation solution (LPR) from their earlier work [12]. Each algorithm involves running the LPR heuristic iteratively with contrasting stopping criteria. Min1 has a time complexity of 3 3 OnNLand ( ) a performance bound of 1 + (1/ 2). Min2 offers a slightly better performance bound, 3/2, but pays a cost in the increased time complexity, 4 3 OnNL ( ). In addition, they offer a heuristic solution for the online version of the Minspace problem. This version requires that decisions concerning the scheduling of individual ads be made without prior knowledge about the ads which will arrive in the

27 17 future. They recommend the First Come Least Full (FCLF) heuristic which schedules each ad, assuming that there is sufficient space, as it arrives in the least full time slots. This algorithm has a performance bound of 2-(1/N). The authors do not test their heuristics. Kumar, Jacob and Sriskanadarajah [13] developed and tested three new techniques for the standard Maxspace problem. First, they proposed the Largest Size Most Full (LSMF) heuristic, which is based on the Multifit algorithm that was developed by Coffman, Garey and Johnson [14] as a solution technique for the classical bin packing problem. This algorithm first finds the maximum slot fullness and then removes ads until a feasible schedule is achieved. The ads are removed based on their relative volume ( ws ) in non-decreasing order. The authors point out that as the problem size grows in i i terms of the number of time slots, N, and the number of advertisements, n, to a size that is comparable to that which is experienced in industry, the basic heuristics that have been proposed are not very efficient. Therefore, they turn to the world of meta-heuristics, proposing a Genetic Algorithm (GA) based solution technique. GAs are directed global search meta-heuristics which are based on the process of natural selection. GA based solutions, in many cases, are extremely successful when applied to global optimization problems. For a more in depth review, please see chapter 4. Lastly, they propose a hybrid algorithm which combines the GA meta-heuristic with the LSMF and SUBSET- LSLF base heuristics. The authors test each of their proposed algorithms and the SUBSET-LSLF algorithm developed by Adler, Gibbons and Matias [6] against two randomly generated data sets. The first data set consists of 40 small problems and the second consists of 150 large problems. The number of time slots for the smaller

28 18 problems ranges from [5, 10]; for the larger problems the range was from [10, 100]. It should be noted that CPLEX was unable to provide an optimal solution for any of the larger problems in reasonable time. As anticipated, although their time requirements were a little more demanding, the meta-heuristic and hybrid models performed extremely well, dominating the performance of the heuristics for both data sets. The hybrid model was the clear winner in terms of solution quality. In its infancy, the industry embraced the CPM pricing model and used it relatively effectively. However, over time many stakeholders recognized one primary difference between online advertising and print advertising which motivated a move to new, more equitable pricing models; a difference in the flow of information. In traditional print media, information primarily flows in only one direction as described pictorially below. Advertiser Publisher Customer Figure 2: Pictorial Representation of Information Flow in Traditional Print Advertising The advertiser provides the publisher with the advertisement and target audience and the publisher provides the advertisements to the customers. At this point, the flow of information, for all intents and purposes is over. This makes it very difficult to analyze the effectiveness of a particular ad campaign. In an effort to overcome this problem, it is common practice to attempt to correlate periodic revenue/sales trends with adaptations to the marketing strategy. However, due to the plethora of potential causal factors, establishing the true level of dependence of the two movements is very difficult and often all but impossible. In contrast, the flow of information in online advertising is bidirectional as described pictorially below.

29 19 Advertiser Publisher Customer Figure 3: Pictorial Representation of the Information Flow in Online Advertising The advertiser provides the publisher with the advertisement and target audience, the publisher provides the chosen customers with the advertisements, and the customers, via their actions, provide the publisher and the advertiser with immediate performance feedback. Common consumer activities which are of particular interest include clicking on the ad, setting up an account with the advertiser, and/or making a purchase. Unlike the interaction in traditional media advertising, this two-way flow of information makes it extremely easy to measure the effectiveness of an online ad campaign. As a result, performance based pricing schemes such as the CPC, CPS or the CPA have become extremely popular as the industry searches for a more equitable risk sharing situation [15]. Several academic researchers have acknowledged this recent industry trend to incorporate performance measures into the pricing models. The authors of papers in the second stream of research have adapted their problem descriptions to account for this performance based pricing scheme. The next series of papers reviewed are all focused on a pure CPC pricing model, and therefore their objective functions attempt to maximize the number of clicks and ignore the amount of space used. Langheinrich et al. [16] assumes that every customer has recently entered search keyword(s) into a search engine and that the publisher has access to this list of keywords. They propose a simple iterative method to estimate the probability of click through c ij for

30 20 each ad/keyword pair based on historical click behavior. Given the resulting probability matrix, they use a linear programming approach to solve the following LP. m i= 1 j= 1 m Max c k d st n i= 1 j= 1 ij i ij kd = h, j= 1,... m m i ij i d = 1, i = 1,..., n ij d 0, i = 1,..., n, j = 1,... m d h i ij ij = probability that ad i will be displayed for keyword j = desired display frequency for ad i m = total number of ads n = number of keywords in the current corpus k i = input probability for keyword i c = click-through rate of ad i for kw j ij The objective of the problem is to maximize the likelihood that the delivered advertisement will be clicked. The first constraint is a frequency constraint which ensures that each ad is served the correct number of times. This is the same constraint that is present in the Maxspace problem. The second constraint makes sure that the display probabilities sum to unity for each keyword. The authors tested their solution model through a series of simulations. Their artificially generated data set had 32 ads and 128 keywords. One potential limitation which is pointed out by the authors is that the model is extremely sensitive to the accuracy of the click through probabilities. This could cause a problem, given the inherent variability of these probability estimates. They propose to avoid the unwanted ad domination by placing a floor for the display probability of each of the ads. This ensures that each ad has some chance of being

31 21 selected. This problem is often referred to as the exploration/exploitation trade-off in academic literature. The test results showed that the proposed method improved the cumulative click through rates by approximately one percent over the random ad selection procedure. This procedure may work well with smaller problems. However, as the number of keywords grows to a realistic size, the search space will become very large, and we would anticipate that the performance of the proposed LP based technique would suffer. This model may be a good choice for a publisher who has selected a pure CPC pricing scheme; however, it lacks several constraints which would limit its realworld applicability. The model fails to limit overselling and fails to prohibit the same ad from being displayed simultaneously in the same banner. Tomlin [17] proposes an alternative nonlinear entropy-based approach to overcoming the exploration/exploitation problem which was mentioned in the work by Langheinrich et al. [16]. Their model avoids unrealistic solutions which only show ads to a very narrow subset of users; however, its applicability is still somewhat limited in that it ignores prevalent space limitations. Similar to the work by Langheinrich [16], Chickering [18] proposes a system which maximizes the click through rate given only advertisement frequency quotas. Instead of using keywords, they partition the ad slots into predictive segments or clusters. Each cluster/ad combination has an associated probability of click through. They use an LP based approach to solve for a maximum revenue generating ad schedule based on these probabilities and the limiting frequency constraints. They also acknowledge the exploration/exploitation problem and attempt to overcome the issue by clustering the click through probabilities. Their method was tested on the msn.com Web site and it

32 22 performed favorably, with respect to time and revenue, against the manual scheduling method that was currently in use. Nakamura and Abe [19] identify several weaknesses of the LP based approach which they proposed in the 1999 work which they co-authored with Langheirrich et al. [16]. The authors propose potential solutions techniques for each of these limitations, including the exploration/exploitation issue, the data sparseness concern, and the multiimpression issue. In an effort to overcome the exploration/exploitation issue, they propose substituting the Gittens Index, an index developed by Gittens [20] which maximizes the expected discounted rewards for the multi-armed bandit problem, in place of the estimated click-though rates within the objective function. They also recommend the use of an interior point LP method, and an alternative lower bounding technique for determining the relative display probabilities. In an effort to deal with the large cardinality of the search space they propose a clustering algorithm for the attributes, thereby reducing the relative problem size. Lastly, they develop a queuing method in an effort to eliminate the possibility of the same ad being shown multiple times in the same banner slot. Similar to their previous work, the authors tested their proposed techniques via a series of simulations on their artificially generated data set. Recall that this data set is relatively small having only 32 ads and 128 keywords. The new technique performed well in comparison with their original LP model and in comparison with a random selection method. The average click-through rates were 5.3%, 4.8% and 3.5% respectively. Yager [21] proposes an intelligent agent system for the delivery of online Web advertisements. The system utilizes a probability-based theme to select the

33 23 advertisements to deliver. The publishers are to share demographic data relative to their Web customers with the advertisers. The advertisers, via a fuzzy logic based intelligent agent, use this information to bid on advertising units with the publisher. The agents iteratively adapt their bids based on the recurrent information relative to the site visitors. The number of units won by a given publisher determines the probability that their ads will be chosen. The publisher then uses a random number generator and the probability matrix to select which advertisements to serve. Unfortunately, Yager s method was not tested. One potential challenge in applying Yager s method is the difficulty in collecting the necessary demographic data. Privacy laws make it very hard to collect good demographic data similar to that which is recommended by the authors. Another method to achieve a similar goal which has come under a little less scrutiny and which may be a promising way to improve advertisement selection is to analyze a customer s surfing behavior. As part of this research, we propose a framework to analyze the raw html from a customer s recent click history using WordNet, a lexical database, and several information retrieval techniques. It is quite evident that the two streams of online advertisement research that we have covered thus far are quite distinct, each having its own primary focus. The first stream is focused on addressing the space limitations of banner advertisement scheduling, taking into account the fact that banner ads are often of different sizes. Given that Web space is at a premium, it is very common for ad prices to vary by size. Therefore, allowing different size ads opens up the market to firms who may not be able to afford the entire banner. While doing so increases revenue, it also creates an obvious scheduling problem which the authors of the first stream have chosen to address. Under a

34 24 pure CPM model, which is the focus of this first stream of research, the advertiser absorbs practically all of the risk. The publisher is paid the same rate regardless of the performance of the ad campaign; therefore, from a revenue maximization point of view, the publisher is just focused on delivering as many ads as possible. This is obviously not an ideal situation for the advertisers. The authors of the papers in the second research stream instead have chosen to focus on the issue of attempting to create a schedule of ads which maximizes a performance based measure and ignores the space constraint. Specifically, these papers focus on the pure CPC pricing model where the publisher is paid a set fee each time a user clicks on an advertiser s ad. Under a pure performance based model such as this, the publisher absorbs all of the risk. The advertiser stands to loose very little regardless of the level of effort which they devote to the relationship. Given that the overall advertisement campaign performance is directly dependent upon the quality of the products provided by both the advertiser (ad, product, offer, etc.) and the publisher (Web site content, incentives, targeting effort, etc.) either of these pure pricing models may lack the correct monetary incentives to maximize the efficiency of the agreement. In an effort to achieve a more equitable risk sharing situation, many companies are adopting a hybrid model which often includes the CPM model and one or more of the performance based pricing schemes. According to industry experts, this type of model enhances the efficiency of the relationship by improving monetary motivation for both parties. We hope to bridge these two streams of research; introducing methodology which addresses both the Web advertisement space limitations and the performance based pricing models.

35 25 Widespread adoption of the performance based pricing models seems to have provided the expected additional motivation. Publishers and advertisers are expending an enormous amount of effort to improve their probability of serving ads to interested consumers, while avoiding inconveniencing those who are uninterested. This is in the best interest of all of the stakeholders (publishers, advertisers and customers). Common efforts include, but are not limited to geographical targeting, content targeting, time targeting, bandwidth targeting, complement scheduling, competitive scheduling and frequency capping (please see chapter 5 for a more detailed description of these practices). The overall goal is to identify a subset of Internet customers who may be interested in a particular advertiser s product and to serve that advertiser s ad accordingly. Given that the average click rate is less than 2%, this is a monumental task; however, as a result of the incredible potential benefits, the devotion of time and effort is well justified. These efforts complicate the task of ad scheduling and therefore need to be considered. In this research, we will extend the current literature by introducing several of these complexities and their resulting formulations while at the same time proposing and testing several artificial intelligence based heuristic and meta-heuristic solution techniques for each model. Current academic research in online advertisement scheduling has provided a solid foundation upon which we can build. The models introduced thus far are still commonly used in industry; therefore, this work is very important. However, since, the vast majority of the industry is attempting, with limited success, to tackle a more complicated mix of these factors, there is quite a bit of work left to be accomplished. We see this as a

36 26 great opportunity for the academic community and therefore will attempt to introduce and provide potential solution techniques for several more complicated models.

37 CHAPTER 3 INFORMATION RETRIEVAL METHODOLOGIES This chapter presents an overview of the field of information retrieval (IR). As this field is a very broad and multidisciplinary, we focus primarily on the subsets which are relevant to our research. In section 3.1, we provide a basic introduction and a general overview of the field of IR research. In section 3.2, we briefly discuss several common data pre-processing methods. In section 3.3, we introduce the vector space model. In section 3.4 we cover structural representation and in section 3.5 we introduce lexical databases with a focused coverage of WordNet. 3.1 Overview Information retrieval (IR) is focused on solving the issues involved with representing, storing, organizing, and providing access to information [22]. The underlying goal of IR is to provide a user with information which is relevant to his or her indicated topic of interest or query. Obviously, this is a very broad and daunting task. Through the early 20 th century, this area of research was of interest to a very small group of people, primarily librarians and information experts. Their goal was to improve the methods by which a library patron was provided information/books which were relevant to his or her topic of interest. However, as a result of many incredible technological advances, the last few decades have seen the focus and reach of IR broaden substantially. No longer are we limited to the information that is available in our local library. Thanks to advances in electronic 27