Data Mining for Management and E-commerce

1. Data Mining for Management and E-commerce By Johnny Lee Department of Accounting and Information Systems University of Utah

2. Agenda • Microeconomic view of Data Mining • A Survey of recommendation systems in E-commerce • Turning Data Mining into a management science tool

3. A Microeconomic View of Data Mining • Kleinberg et al. 1998 • Research Question: What is the economic utility of data mining? How to determine whether DM result is interesting?

4. A Microeconomic View of Data Mining • “Interesting Pattern” • Confidence and support • (High balanceHigh income) • Information content • ? • Unexpectedness • (Super ball result  stock price) • Actionability • $,$,$….

5. A Microeconomic View of Data Mining • Value of data mining • computing power and data un-aggregate optimization • Study of intricate ways (correlation and clusters in data that affect the enterprise’s optimal DECISION

6. A Microeconomic View of Data Mining Value of DM Firm max f(x) yi=customer data

7. Example one If (demand of Beer) is not related (demand of diapers) then NO DM If (demand of beer +demand of diaper)=(supply of beer-demand of beer)+*(supply of diaper- demand of diaper)+ then DM is needed

8. Example 2 Phone rate and users without Data mining experimenting arbitrary clusters with data mining optimize the profit by best matching customers and strategies

9. Example 3 • Beer and diaper a~~gain • Mining to decide how to jointly promote items. • Mining data in rows or columns • Goal oriented What is the goal? Generated revenue • Conflict in action space, what to do?

10. Contribution • Automatic pattern filtering system based on economic value • Rules for manual pattern filtering system • Rules for determine trigger point of Data Mining

11. A survey of recommendation systems in electronic commerce • Wei et al. 2001 • Research question: What are the types of E-commerce recommendation systems and how do they work?

12. E-commerce recommendation Systems • Suggest items that are of interest to users based on something. • Something: • Customer characteristics (demographics) • Features of items • User preferences: rating/purchasing history

13. Framework for Recommendation

14. Types of Recommendation • Prediction on preference of customers Personalized and non personalized • Top-N recommendation items for customers Personalized and non personalized • Top-M users who are most likely to purchase an item

15. Classification of Recommendation Systems • Popularity-based: best sell • Content-based: similar in items features • Collaborative filtering: similar user’s taste • Association-based: related items • Demographic-based: user’s age, gender… • Reputation-based: Represent individual • Hybrid

16. Popularity-based

17. Procedures of Content-based • Feature extraction and Selection • Representation item pool by feature decided • User profile learning • Recommendation

18. Content-based

19. User Profile Learning • pim=preference score of the user I on item m • wi=coefficient associated with feature j • fmj=the value of the j-th feature for item m • b=bias

20. Collaborative Filtering • Recommend items based on opinions of other similar users • Dimension reduction by trimming preference matrix • Neighborhood formation for most similar user(s) • Recommendation generation

21. Collaborative filtering

22. Neighborhood Formation • Pearson correlation coefficient • Constrained Pearson correlation coefficient • Spearman rank correlation coefficient • Cosine similarity • Mean-square

23. Neighborhood Selection • Weight threshold • Center-based best-k neighbors • Aggregate-based best-k neighbors

24. Recommendation Generation • Weighted average • Deviation-from-mean • Z-score average

25. Association-based • Item-correlation for individual users • Similarity computing • Recommendation generation • Association Rules • Guns and ammunition • Cigarette and lighter • Paper plate and soda Theory: Complementary goods? No theory: Co-occurrence?

26. Association-based Pui=preference score of user u on item I Pibar=average preference sore of the I-th item over the set of co- rate user U Pubar=average of the u-th user’s preference score

27. Association Based

28. Demographics-based • Items that customers with similar demographics characteristics have bought • Teens marketing • Data transformation: Counting, Exp(# of items), Statistic based • Category Preference model learning • Recommendation generation

29. Demographics-based Methods: • Counting-based (frequency threshold) • Expected-value-based method • Statistics-based method

30. Comparison of recommendation approach

31. Contribution • Provide a systematic way to choose from E-commerce recommendation systems for practitioners • Lay out existing approach

32. BREAK

33. Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results • Cooper & Giuffrida 2000 • Research question: How can we improve the performance of PromoCast (or other market) Forecast system by adding some local adjustment parameters?

34. Terminology • SKU: Stock keeping unit • KDS: knowledge discovery using SQL) • Management science: ??????????????

35. KDS

36. Rule network example

37. Activated Nodes example

38. Corrective Action U_12= 0 U4-11= 58 U_3= 221 U_2= 1149 U_1= 3583 Ok= 1115 O_1= 7 O_2= 1 O_3= 0 O_4_11= 0 O_12= 0

39. KDS • Bottom-up: start from the input database • No Memory-Bound processing • Minimal data preprocessing • Separates the learning phase from the action phase • Evaluation: for 10117 cases 8.9% ($?)

40. KDS • Is this a research? Is this a case study? • Is this a management research? • Why should I know about it as a researcher/manager/engineer?

41. Acknowledge • All right of trade marks and web-site contents belongs to the lawful owners