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Why Profit Mining?
A major obstacle in data mining application is the gap between:– statistic-based pattern extraction and
– value-based decision making
Profit mining:– value-based data mining
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An Example Suppose we want to maximize profit. Association
rules [AIS93]
{Perfume}->Lipstick (more often)
{Perfume}->Diamond (more profit)
do not suggest which items (and prices) to
recommend to a customer who bought Perfume.
Similar problems with correlation, classification, etc.
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The Problem
Given: several transactions of form:– {<I,P,Q>,…, <I,P,Q> | <I,P,Q>}, for Item,
Promotion code, and Quantity. | separates non-target items and target items.
– {<FlakedChick., $3,2> | <Sunchip,$1,1>}
Recommend target <I,P> to customers who buy non-target items, to maximize profit.
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Not Prediction Problem
An example:– 100 customers each bought 1 pack for $1/pack.
Profit=100(1-0.5)=$50.
– 100 customers each bought 4 packs for $3.2/4-pack. Profit=100(3.2-2)=$120.
Prediction repeats the history. Profit mining gets smarter from the history, by recommending “right items” and “right prices”.
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Challenge I - notion of profit
Pure statistic approach favors– {Perfume}-> Lipstick
Pure profit approach favors– {Perfume}-> Diamond.
Profit mining considers:– both statistical significance and profit significance.
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Challenge II - customer intention
Mining On Availability (MOA):– Paying a higher price implies the willingness to
pay a lower price.
{<FC,$3>} -> <Sunchip,$1> can be extracted from transaction {<FC,$5> | <Sunchip,$1.5>}
Recognizing this behavior brings new sales opportunities (at lower price).
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Challenge III - search space
Thousands of items, and much more sales. Any combination can trigger a recommendation.
Search at alternative concepts (food, meat, etc) and prices makes it worse.
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Step 1: generating rules
Association rules – {Diaper -> Beer}, supp=10%, conf=80%
Recommendation rules:– {g1,…,gk} -> <I,P>, where gi is <Item,Price>, or Item,
or Concept.
– {<FlakedChick. , $3.8>} -> <Sunchip,$4.5>
– {FlakedChick.} -> <Sunchip,$4.5>
– {Meat} -> <Sunchip,$4.5>
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Step 2: building the model
We rank rules by the “average profit” made by the recommendation of a rule. – {<FC,$3.5>} -> <Sunchip,$1> matches
t1: {<FC,$4.0>| <Sunchip,$2>} (a hit) t2: {<FC,$4.5>|<Milk,$3.5>} ( a miss)
– If the cost of Sunchip is $0.7, the average profit is $0.15. To recommend, we select the matching rule of the
highest possible rank.
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Step 3: Pruning the model
The model favors “high average profit” rules.
Such rules may bring a large profit. Such rules may be random noise. Cannot prune them simply based on
statistical frequency.
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Pruning the model
We prune rules to increase the estimated profit on the whole population.
We organize rules into specificity tree: the parent is the highest ranked general rule of a child.
We cut off the tree to maximize the estimated profit.
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Evaluation Synthetic datasets: IBM synthetic data generator,
modified to have price and cost. 1000 items and 1000K transactions For non-target item i:
– cost(i)=c/i
– price j=(1+j*10%)cost(i), j=1,2,3,4. For target items:
– Dataset I has 2 target items
– Dataset II has 10 target items
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