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

Agenda 1.Microeconomic view of Data Mining 2.A Survey of recommendation systems in E-commerce 3.Turning Data Mining into a management science tool

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

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

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

A Microeconomic View of Data Mining Value of DM Firm  max f(x) y i =customer data

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

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

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?

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

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

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

Framework for Recommendation

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

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

Popularity-based

Procedures of Content-based 1.Feature extraction and Selection 2.Representation item pool by feature decided 3.User profile learning 4.Recommendation

Content-based

User Profile Learning p im =preference score of the user I on item m w i =coefficient associated with feature j f mj =the value of the j-th feature for item m b=bias

Collaborative Filtering Recommend items based on opinions of other similar users 1.Dimension reduction by trimming preference matrix 2.Neighborhood formation for most similar user(s) 3.Recommendation generation

Collaborative filtering

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

Neighborhood Selection Weight threshold Center-based best-k neighbors Aggregate-based best-k neighbors

Recommendation Generation Weighted average Deviation-from-mean Z-score average

Association-based Item-correlation for individual users 1.Similarity computing 2.Recommendation generation Association Rules –Guns and ammunition –Cigarette and lighter –Paper plate and soda Theory: Complementary goods? No theory: Co-occurrence?

Association-based P ui =preference score of user u on item I P i bar=average preference sore of the I-th item over the set of co- rate user U P u bar=average of the u-th user’s preference score

Association Based

Demographics-based Items that customers with similar demographics characteristics have bought –Teens marketing 1.Data transformation: Counting, Exp(# of items), Statistic based 2.Category Preference model learning 3.Recommendation generation

Demographics-based Methods: 1.Counting-based (frequency threshold) 2.Expected-value-based method 3.Statistics-based method

Comparison of recommendation approach ApproachInput infoTypes of recommendation Degree of Personalization Popularity-based User preferencesTop-NNon-Personalized Content-basedFeatures of items and individual user preferences Prediction, top-N and top-M users Personalized Collaborative Filtering User preferencesPrediction top-N recommendation Personalized Association-basedUser preferencesPrediction top-N recommendation Personalized Demographics-basedUser demographic &preferences,feature s of items Prediction top-N & top-M Personalized Reputation-basedUser preferences & reputation matrix top-N & possible prediction Personalized

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

BREAK

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?

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

KDS

Rule network example

Activated Nodes example

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

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 cases  8.9% ($?)

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?

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