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Application of Decision-TreeInduction Techniques to

Personalized Advertisements onInternet Storefronts

Paper By:

Source:

International Journal of Electronic Commerce/ Spring 2001, VoL5, No.3, pp. 45-62. Copyright 2001 M.E. Sharpe, Inc. Allrights reserved.

Jong Woo Kim, Byung Hun Lee, Michael J. Shaw, Hsin-LuChang, Matthew Nelson

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Source: International Journal ofElectronic Commerce 5 no3 45-62Spr 2001

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Paper Authors: JONG WOO KIM is an assistant professor in the

depart-ment of statistics at Chungnam NationalUniversity in Teajon, Korea.

BYUNG HUN LEE is a researcher in marketing at MPC

LTD. in Korea High-level timing goals. MICHAEL J. SHAW is director of the Center for

Information Systems and Technology Management atthe University of Illinois at Urbana-Champaign.

HSIN-LU CHANG is a Ph.D. student in informationsystems at the School of Commerce, University of

Illinois at Urbana-Champaign. MATTHEW NELSON currently is a second-year Ph.D.student in information systems at the University ofIllinois.

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Paper References:1. Agrawal, R.; Aming, A.; Bollinger, T.; Mehta, M.; Schafer, J.; and Srikant, R. The Quest data

mining system. In Proceedings of the 2nd International Conference on Knowledge Discovery inDatabases and Data Mining. Portland, OR, 1996, pp. 244-249.

2. Allen, c.; Kania, D.; and Yaeckel, B. Internet World Guide to One-to-One Web Marketing. NewYork: John Wiley, 1998.

3. Berry, M.J .A., and Linoff, G. Data Mining Techniques for Marketing, Sales, and CustomerSupport. New York: Wiley Computer Publishing, 1997.

4. BroadVision. A BroadVision one-ta-one white paper. White paper, BroadVision (1996).5. BroadVision. http://broadvision.com (2000).6. Business Week Graphic: On-line sales are soaring. Business Week(June 22, 1998) (based on

data from Forrester Research).7. Chaturvedi, A.R; Hutchinson, G.K.; and Nazareth, D.L. Supporting complex real-time decision

making through machine learning. Decision Support Systems, 10, 2 (September 1993),213-233.

8. CLIPS. CLIPS Riference Manual (Version 6.05). CLIPS, 1997.9. Garbonell, J.G., and Michalski, R.S. Machine learning: A historical and methodological analysis.

Al Magazine (fall 1983), 69-79.10. Gupta, 0.;, Digiovanni, M.; Norita, H.; and Goldberg, K. Jester 2.0: Evaluation of a new linear

time collaborative filtering algorithm. 22nd International ACM SIGIR Conference on Researchand Development in Informa-tion Retrieval(August 1999), pp. 291-292.

11. Johnson, R.A., and Wichern, D.W.Applied Multivariate Statistical Analysis. Englewood-Cliffs, NJ:Prentice Hall International, 1992.

12. Kim, ]., Lee, K., Shaw, M. J., Chang, H., and Nelson, M. A preference scoring tedmique topersonalized advertisements on Internet storefront. Working paper, University of lllinois atUrbana-Champaign (2000).

13. Lee, K. Personalized advertisement techniques for one-to-one marketing on Internet stores.Master's thesis, Chungnam National University, 2000.

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Reference for the paper: http://an5qy7ag4q.scholar.serialssolutions.com/?sid=google&auinit=J

W&aulast=Kim&atitle=Application+of+Decision-Tree+Induction+Techniques+to+Personalized+Advertisements+on+Internet+Storefronts&title=International+journal+of+electronic+commerce&volume=5&issue=3&date=2001&spage=45&issn=1086-4415

FOR MORE INFO...

STONY BROOK UNIVERSITY LIBRARIES

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Introduction This paper studies personalized recommendation

techniques that suggest products or services to thecustomers of Internet storefronts based on their

demographics or past purchasing behavior

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Currently available techniques Currently available recommendation

techniques are primarily based: Collaborative filtering

Preference scoring

Rule-based approaches

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Currently available techniquescont.

Collaborative filtering selects advertisements forcustomers based on the opinions of other customerswith similar past preferences.

The preference-scoring approach to personalized

recommendation uses a preference-score concept toselect personalized advertisements based on initialcustomer profile, purchase history, and behavior inInternet stores

In the rule-based approach, marketing rules from

marketing experts are a core component in providingpersonalized advertisements.

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Rule based approach Based on the marketing rule that

undergraduates in Indiana whose

income is greater than $80000 preferto own Luxury cars, caradvertisements are displayedwhenever customers who live in

Indiana access the Internetstorefront.

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Rule based approach

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Source: International Journal ofElectronic Commerce 5 no3 45-62

Spr 2001

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Issues in Rule based approach The marketing rules for personalized

recommendation usually come frommarketing experts in that domain.

The acquisition of marketing rules, however, isunsystematic, time-consuming, and dependenton the intuition of marketing experts.

The marketing rules in a knowledge base need

to be continuously updated and changed, butmanual change management is a difficult task.

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Spr 2001

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Proposed Solution To overcome the defects of the rule-

based approach in personalizedadvertisements, the authors proposedecision-tree induction techniquesthat extract marketing rules fromdatabases in Internet stores.

Rule-based personalization has twophases: marketing-rule extraction, and

real-time advertisement selection

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Spr 2001

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Proposed Solution In order to obtain valuable marketing rules,

decision tree induction technique is used toanalyze purchase-transaction histories,

customer profiles, and product information.

The extracted marketing rules are storedin a marketing-rule base and are used forreal-time personalized-advertisement

selection when customers visit the Internetstore

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Proposed Solution Schematic

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Marketing Rule Extraction The extraction of

useful marketing rulesusing decision-tree

induction techniquesbegins by defining ahierarchy tree ofproduct categories toextract marketing

rules at variousabstraction levels ofproduct categories

MP3 Files Sporting goods

Pop MusicClassical Music

Symphony ConcertRap

Dance Metal

Shakira Britany

Kim Rolla

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Marketing Rule Extraction The rule-extraction phase has four

steps:

(1) selecting learning data, (2) generating target variables,

(3) constructing a decision tree, and

(4) selecting a marketing rule

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Spr 2001

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Marketing Rule Ext. Phase 1 Step 1

Training data sets and test data sets are

selected from customer records, makingit possible to begin with a data set ofmanageable size. The size of a data setis determined by such factors as numberof customer-profile records, the volume

of disk storage, and the computingcapacity of the inductive learning tools.

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In the next step, values of targetvariables are generated for the selected

data records. In the present approach,target variables are whether or not acustomer prefers a specific productcategory in the hierarchy tree. Since thetarget variables do not exist in thedatabases, they must be generated. Thisis done using the purchase-transactiondatabase.

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Spr 2001

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Marketing Rule Ext. Phase 1 For example, customer profile records can have the following

attributes:

CUSTOMER =

After the target variables are generated, CUSTOMER relation isextended as follows.

CUSTOMER' =

where t_MP3_file, Etc. are binary variables to specify a customer'spreference for the specific product categories. That is, the binaryvariable is assigned to 1 when the customer prefers thecorresponding product category.

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The third step is decision-tree construction.Using inductive learning tools and the training

data set, decision trees are inducted for all thetarget variables.

Step 4

After the decision tree is constructed, useful

marketing rules are filtered from constructeddecision-trees based on the validation resultsfrom test data sets and accuracy of the rules.

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Marketing Rule Ext. Phase 1

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TARGET-VARIABLE GENERATION

There are several possible ways togenerate target variables based on

the purchase-transaction database: (1) the counting-based method,

(2) the expected-value-based method,

(3) the statistics-based method, and

(4) the subcategory-based method.

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Spr 2001

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Counting-based method The counting-based method, based on the number of

purchases in a specific product category, makes itpossible to decide whether or not the customerprefers the product category.

That is,PRE[subij] = 1 if NP[subij] >= Omega

where, NP[subij] = the number of purchaseof customer i for product category jOmega is a minimum threshold,

which is determined by analysts.PRE[subij] = 0 otherwise

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Spr 2001

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Expected-value-based method

N[subj] is the number of products in product category j.

Alpha is a multiplier for the expected value. Usually alpha isequal to or greater than 1. When alpha is 1, if customer ipurchased more items in product category j than theexpected number of purchases of product category j, thismeans that customer i prefers product category j

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Statistics-based methodStatistical values like the mean, the median, the firstquartile, and the third quartile are used to generatetarget variables

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Spr 2001

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Subcategory based method In the case of non-leaf-node product

categories, the corresponding targetvariables can be generated based on

subcategory target variable values (whenthe target variables for subcategories arealready generated using one of the abovemethods).

PRE[subij] = 1 if PRE[subik] = 1 for some

subcategory k of jPRE[subij] = 0 otherwise

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Spr 2001

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MARKETING-RULE SELECTION A decision tree includes marketing rules that link customer

demographics with preferences for product categories.Since marketing rules may have low predictability or maynot be accurate, the valuable marketing rules have to beselected from constructed decision trees.

Two phase heuristics are used for marketing-rule selection.1. Choose decision trees whose predictability (1 -misclassification rate), with respect to a training data set, isgreater than a certain threshold.

2. Select nodes from the filtered decision trees whoseaccuracy (i.e., purity) is greater than a certain threshold.

The selected nodes in the decision trees are translated inthe rule structure. Here is an example of a translated rule.

If age < 30 and gender = male then Ballad, with accuracy= .9 and level = 3.

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ADVERTISEMENT SELECTION Personalized advertisements are selected

on a real-time basis.

Let M be the number of advertisements tobe displayed in an Internet storefront, andL the depth of the product-categoryhierarchy tree.

The advertisement-selection algorithm is as

follows. STEP 1 m: = 0,l: = L, generate fact set using

the specific customer's profile.

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Source: International Journal of

Electronic Commerce 5 no3 45-62Spr 2001

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Advertisement Selection Algorithm Continued. STEP 2 Until m < M and 1 > 0

2.1 Fire marketing rules of level 1 withcustomer's facts.

2.2 If there are matching rules, matched rules

are fired by accuracy order. The productcategories appeared in consequent parts of therules are selected as preferred productcategories for the specific customer. Chooseadvertisements for that category from theadvertisement database. The number of selected

advertisements should be less than M - m. 2.3 If m = M, exit. 2.4 Update m to reflect the additionally selected

advertisements, and update l to l-1.

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Advertisement Selection Algorithm Continued. STEP 3

If m < M, randomly select M - m advertisementsfrom the advertisement database

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EXPERIMENT EXPERIMENTAL DATA COLLECTION

Data from 330 respondents were gathered. At first, the respondentswere asked to select preferred product categories in MP3 music filesand sporting goods or leisure equipment. Then they were asked tochoose five MP3 music files among the 32 displayed products. Thedisplayed products were equally selected from all MP3 leaf nodes. The

respondents were also asked to purchase five sporting goods or leisureequipment items among the 24 displayed products. The displayedproducts were equally selected from all sporting goods or leisureequipment leaf nodes (three products from each of the eight leafnodes). On the next two pages, respondents were asked to rank MP3music files and sporting goods/leisure equipment items on a five-pointpersonal-interest scale (1 = low interest to 5 = high interest). 16advertisements for MP3 music files were displayed to rank interestsabout the advertisements, which were selected one item from each leafnode. In addition, 16 advertisements for sporting goods or leisureequipment items were displayed to rank interests about those, whichwere two selected items from each leaf node. Finally, the respondentswere asked to specify their profiles, including age, gender, job, and soon.

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MARKETING-RULE EXTRACTION

The 330 respondents were divided into two data sets,one for marketing-rule generation, and the other fortesting the effectiveness of the proposed approach.For marketing-rule generation, 198 responses (60%of the total data set) were used. Among the 198 data

items, 70 percent were used to construct decisiontrees, and 30 percent to validate constructed decisiontrees. For target-variable generation, an expectedvalue-based approach of alpha= 1 was used. All told,37 decision trees were constructed (26 for MP3 musicfiles and 11 for sporting goods or leisure equipment).

Decision trees with predictability greater than 65percent were selected in the marketing-rule selectionstep. Decision rules with accuracy greater than 65percent were selected as valuable marketing rules.

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EFFECTIVENESS TEST

To test the effectiveness of the proposed decision-treeinduction approach, the results were compared to thepreference-scoring approach and a random selection. Thedata of 132 respondents (40% of the total data set) wereisolated for effectiveness testing. Three advertisements

were selected for each respondent: that is, oneadvertisement using each of the three approaches for eachrespondent. In the case of the decision-tree inductionapproach, the marketing rules generated earlier were usedto select personalized advertisements. In the case of thepreference-scoring approach, advertisements were selectedbased on the respondents' own interest-manifestationbehavior (purchase history, profile, preferred productcategories). In the case of random selection,advertisements were selected on a simple random basis.The means of the five-point-scale personal-interest scoresfor the selected advertisements were compared statistically

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RESULT

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Conclusion Statistically, there were differences among the five-point-scale

scored means of customer' interest in advertisements from thethree different approaches: decision-tree induction, preferencescoring, and random selection. Thus, the effectiveness ofpersonalized-recommendation techniques is affected by thedecision algorithm utilized in the personalized- advertisement

selection process. The proposed decision-tree induction approach is effective in

generating marketing rules as a means to assist rule-basedpersonalized recommendations.

In the case of sporting goods or leisure equipment, decision-treeinduction gave the best results. But in the case of MP3 music files,preference scoring gave the best results. This indicates that theappropriateness of personalized-advertisement techniquesdepends on the characteristics of product categories. Thus it isnecessary to study how various personalized-advertisementtechniques can be used cooperatively.

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ThankYou

Next Presentation 5/2/2006

Topic - New Advances in Data Mining

By:

Group 14

Madhavarapu,Chidroop

Sandhuria,Deepanshu

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