1. Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts 2011/10/04 Source: International Journal of Electronic Commerce (2001) 授課老師 : 楊婉秀 教授 學 生 : 林慧玉 M0061014

2. Outline 2  Abstract 1.Introduction 2. Related Works 2.1 Rule-Based Personalized-Recommendation Techniques 2.2 Decision-Tree Induction Techniques 3. Marketing Rule Extraction and Advertisement Selection 3.1Using Inductive Learning Techniques for Marketing-Rule Extraction 3.2 Marketing-Rule Extraction 3.2.1Target-Variable Generation 3.2.2 Marketing-Rule Selection 3.3 Advertisement Selection 4. Experiment 4.1 Experimental Design 4.1.1 Experimental Data Collection 4.1.2 Marketing-Rule Extraction 4.1.3 Effectiveness Test 4.2 Result 5. Conclusion

3. Abstract  Customization and personalization services are a critical success factor for Internet stores and Web service providers.  This paper studies personalized recommendation techniques that suggest products or services to the customers of Internet storefronts based on their demographics or past purchasing behavior.  This paper proposes a marketing rule-extraction technique for personalized recommendation on Internet storefronts using machine learning techniques, and especially decision-tree induction techniques. 3

4. 1. Introduction 1/4  The pace of the revolution to the global electronic economy is increasing rapidly. Business-to-consumer electronic commerce is growing in every category of goods.  One-to-one marketing (also known as database marketing and relationship marketing) introduces a fundamental new basis for competition in the marketplace by enabling organizations to differentiate based on customers rather than products. (Peppers, D., and Rogers, M.,1993)  To maintain a persistent relationship with an individual consumer, customer databases and interactive communication channels are used as implementation tools for one-to-one marketing. (Allen, C.; Kania, D.; and Yaeckel, B.,1998) 4

5. 1. Introduction 2/4  Personalized-recommendation techniques suggest products or services based on customer preferences or customer behavior in Internet stores.  Currently available recommendation techniques are primarily based on collaborative filtering, preference scoring, or rule-based approaches.  Collaborative filtering selects advertisements for customers based on the opinions of other customers with similar past preferences. 5

6. 1. Introduction 3/4  The preference-scoring approach to personalized recommendation uses a preference-score concept to select personalized advertisements based on initial customer profile, purchase history, and behavior in Internet stores. (Kim, J., Lee, K., Shaw, M. J., Chang, H., and Nelson, M.,2000)  Broad Vision’s One-to-One™ System uses a rule-based matching technique to provide appropriate advertisements to customers and has been applied to Internet radio, Internet television, and Internet banking. (Broad Vision.,1996 ； 2000)  In the rule-based approach, marketing rules from marketing experts are a core component in providing personalized advertisements. The effectiveness of rule-based approaches mainly depends on the quality of the knowledge in the rule base. 6

7. 1. Introduction 4/4  Machine learning, an integral part of artificial intelligence, refers to the class of computational methods for deriving insightful knowledge (including heuristics, strategies, and structure) from data, observation, or past solutions. (Shaw, M.,1993)  The present study is motivated by the idea that machine- learning techniques can contribute to the solution of knowledge-acquisition and –maintenance problems in the rule-based approach to personalized recommendation. 7

8. 2. Related Works  In the rule-based approach, marketing rules in a knowledge base are a core component of personalized recommendation. 8 2.1 Rule-Based Personalized-Recommendation Techniques

9. 2.2 Decision-Tree Induction Techniques 1/3  Inductive learning methods, as one type of machine learning technique, are used to infer rules of classification by analyzing examples from a domain. (Tsai, L.-H., and Koehler, G.J.,1993)  As a means of classification or prediction, decision-tree induction techniques construct decision trees to discriminate among classes of objects.  An inducted decision tree is a set of nested if-then statements.  The goal is to build a tree that will make it possible to assign a class to the target variable of a new instance based on the values of the other fields or independent variables. 9

10. 2.2 Decision-Tree Induction Techniques 2/3 10

11. 2.2 Decision-Tree Induction Techniques 3/3  The predictability of a constructed decision tree is tested using subsequent cases, usually called the test data set, whose correct classification has been observed.  Machine learning seeks to acquire knowledge about a specific domain from available data in an automated manner.  Knowledge acquired from learning techniques can be a valuable way to understand customers’ online behavior in Internet stores and to gain competitive strength. 11

12. 3. Marketing Rule Extraction and Advertisement Selection 1/2  Rule-based personalization has two phases: (1)marketing-rule extraction. (2) real-time advertisement selection.  In order to obtain valuable marketing rules, inductive learning techniques are used to analyze purchase-transaction histories, customer profiles, and product information. 12 3.1Using Inductive Learning Techniques for Marketing- Rule Extraction

13. 3.1Using Inductive Learning Techniques for Marketing-Rule Extraction 2/2 13

14. 3.2 Marketing-Rule Extraction 1/2 14

15. 3.2 Marketing-Rule Extraction 2/2  The rule-extraction phase has four steps: (1) selecting learning data. (2)generating target variables. (3) constructing a decision tree. (4) selecting a marketing rule. 15

16. 3.2.1Target-Variable Generation 1/5  There are several possible ways to generate target variables based on the purchase transaction database: (1) The counting-based method. (2) The expected-value-based method. (3) The statistics-based method. (4) The subcategory-based method. 16

17. 3.2.1 Target-Variable Generation 2/5  The counting-based method, based on the number of purchases in a specific product category, makes it possible to decide whether or not the customer prefers the product category. 17

18. 3.2.1 Target-Variable Generation 3/5  The expected-value-based method makes it possible to determine whether or not a specific customer prefers a product category, as follows. 18

19. 3.2.1 Target-Variable Generation 4/5 19  In the statistics-based method, statistical values like the mean, the median, the first quartile, and the third quartile are used to generate target variables.  When the mean is used, target variables can be generated using formula.

20. 3.2.1 Target-Variable Generation 5/5  In the case of non-leaf-node product categories, the corresponding target variables can be generated based on subcategory target variable values 20

21. 3.2.2 Marketing-Rule Selection 1/2  Marketing rules may have low predictability or may not be accurate, the valuable marketing rules have to be selected 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, is greater than a certain threshold. (2) Select nodes from the filtered decision trees whose accuracy (i.e.,purity) is greater than a certain threshold. 21

22. 3.2.2 Marketing-Rule Selection 2/2  EX : If age < 30 and gender = male then Ballad, with accuracy =.9 and level = 3.  Accuracy information and level information will be used to determine the firing sequences of the marketing rules during the real-time advertisement-selection phase. 22

23. 3.3 Advertisement Selection 1/3  Personalized advertisements are selected on a real-time basis using customer profiles and extracted marketing rules.  Among the same-level rules, higher-accuracy rules have a priority in rule firing.  Let M be the number of advertisements to be displayed in an Internet storefront, and L the depth of the product- category hierarchy tree. 23

24. 3.3 Advertisement Selection 2/3 24 Figure 6. Implementation of Advertisement-Selection Algorithm

25. 3.3 Advertisement Selection 3/3 25 Figure 6. Implementation of Advertisement-Selection Algorithm

26. 4. Experiment 1/3 26 4.1 Experimental Design 4.1.1 Experimental Data Collection Figure 7-1. Experiment Web Pages

27. 4.1.1 Experimental Data Collection 2/3 27 Figure 7-2. Experiment Web Pages

28. 4.1.1 Experimental Data Collection 3/3 28 Figure 7-3. Experiment Web Pages

29. 4.1.2 Marketing-Rule Extraction 29  The 330 respondents were divided into two data sets, one for marketing-rule generation, and the other for testing 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 decision trees, and 30 percent to validate constructed decision trees.  Decision trees with predictability greater than 65 percent were selected in the marketing-rule selection step.  Decision rules with accuracy greater than 65 percent were selected as valuable marketing rules.

30. 4.1.3 Effectiveness Test 30  The decision-tree induction approach, the marketing rules generated earlier were used to select personalized advertisements.  The preference-scoring approach, advertisements were selected based on the respondents’ own interest- manifestation behavior (purchase history, profile, preferred product categories) versus the inducted marketing rules utilized in the decision-tree induction approach.  In the case of random selection, advertisements were selected on a simple random basis.

31. 4.2 Result 1/3 31

32. 4.2 Result 2/3 32

33. 4.2 Result 3/3  Statistically, there were differences among the five-point-scale scored means of customer’ interest in advertisements from the three different approaches: decision-tree induction, preference scoring, and random selection. Thus, the effectiveness of personalized- recommendation techniques is affected by the decision 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-based personalized recommendations.  In the case of sporting goods or leisure equipment, decision- tree induction gave the best results. But in the case of MP3 music files, preference scoring gave the best results. This indicates that the appropriateness of personalized- advertisement techniques depends on the characteristics of product categories 33

34. 5. Conclusion 1/2 34  For sporting goods and leisure equipment products, decision-tree induction gave the best results.  For MP3 music products, preference scoring gave the best results and decision-tree induction ranked next.  The proposed decision-tree induction approach can be used effectively for personalized recommendation.  They also imply that the appropriateness of personalized- recommendation techniques is affected by characteristics of the product categories and other aspects of Internet stores.

35. 5. Conclusion 2/2 35  Machine-learning techniques make it possible to acquire valuable information for understanding on-line customer behavior.  This information can be used for personalization, designing user interfaces for storefronts, developing intelligent customer service, and formulating marketing strategies.

36. - THE END - 36 Thanks for your listening