1. ISQS 7342 Dr. Zhangxi Lin By: Tej Pulapa

2. Recommendation System Predict consumer behavior - Is to discover the relationship between one’s personal characteristics, e.g. age, gender, hometown, and the probability that one will respond to a recommendation. Such relationships can be used to recommend those customers with a matching profile that have the highest probability of responding. Consumer Profiles have to be developed and updated constantly based on the historical purchasing behavior of the consumers, paying attention to season is also an important issue to prevent recommendation system jokes on SNL.

3. Multi Dimensional Profile Identifiers Now that we have the following Historical data of consumer purchasing patterns Demographic data Income Based on the above available information several profiles can be deduced, lets assume each profile is identified by a arbitrary identifier for eg., poor, middle class, rich or online shopper or city dweller, suburban dweller, or a techie gadget lover who can spare his food. What about a poor, online shopper residing in a suburban area of Hollywood who has skipped dozens of meals to buy a I phone?

4. 15432 Unique Profiles – idealistic condition Constant changes in consumer wants A consumer may belong to all the profiles 1,2,3,4 and 5 partially but with certain % of agreement with each of the 5 profiles. 10% of 1, 15% of 2, 35% of 3, 30% of 4 And 10% of 5, in this case a new profile is necessary but it also important to consider that profile 1 Is in agreement to profile 2 and 3 with 20% and 25%.

5. This bears a large degree of complexity to the process of profiling consumers. Each profile can be viewed as a composition of more than one element/ attribute. Deducing the available data into basic elements is essential which can be perceived at this point as reaching the leaf of a decision tree with ideally zero entropy.

7. Decision Trees Instances describable by attribute- value pairs Target function is discrete valued Disjunctive hypothesis may be required Possible noisy training data In building a decision tree, we aim to decrease the entropy of the dataset until we reach leaf nodes at which point the subset that we are left with is pure, or has zero entropy and represents instances all of one class (all instances have the same value for the target attribute). Entropy – synonym with impurity or disorders in the data

8. Decision Trees with Association rule methods X  Y where X is a set of items and Y is a single item Association rule methods are initial data exploration approach Use with decision trees: With use of variable transformation node and formula builder, creating new variables that reflect basic association rule concept X U Y  Z

9. Respond * GradStud >= 15 Where Respond is binary and GradStud is a % of Population 25 years and over and Graduate or professional degree and the value 15 is the mean value. Hence you have a variable of upper half of population with a degree and above age of 25 who responded to the recommendation. TENURE >= 24 & ExpHouse > 0 or PlusCar >3 = loyal_rich_cust Tenure – months since first purchase ExpHouse – homes more than $300K PlusCar – extra number of cars

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