Transcript Personalization & Nearest Neighbor

Business Intelligence Technologies –
Data Mining
Lecture 5 Personalization, k-Nearest
Neighbors
1
Agenda

Personalization

K-Nearest Neighbors

Collaborative Filtering

Case Discussion

Software Demo
2
Personalization
Personalization/customization tailors certain
offerings by providers to consumers based
on knowledge about them with certain goals
in mind.
Personalized
offerings
Customer
How?
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What is Currently Being Personalized

Personalized recommendations of products and services


Personalized products for individual consumers



e.g., custom-made CDs, Dell computers
Personalized emails
Personalized content



e.g., recommend books, CDs and vacations;
e.g., Yahoo’s personalized home page
Amazon’s channel management
Personalized (dynamic) prices
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Sample Automated E-mail
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Personalization Process
Understand-Deliver-Measure Cycle
Adjusting Personalization Strategy
Measuring Personalization Impact
Feedback loop

Delivery and Presentation
Matchmaking
Building Consumer Profiles
Measure
Impact of
Personalization
Deliver
Personalized
Offerings
Understand
the Consumer
Data Collection
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Building Profiles from Data

Data Needed
 Personal information, preferences & interests
 Registration data, including demographic data
 Customer ratings
 Purchasing data
 What was bought, when and where
 Browsing & visitation data
 Clickstream (Weblog files)

Building customer profiles
 Demographic
(e.g., name, address, age)
 Behavioral (e.g., favorite type of book – adventure,
largest transaction - $295)
 Things learned from data
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Matchmaking Problem

Example: Large e-Commerce Site
 10M
customers
 1M products
 Question: How to match (target) the products
to individual customers? What 10 books (out
of 1M) should I show to Jane on her
homepage?
 Solution: To do matchmaking, use
customer profiles
 various recommendation technologies

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Recommendation Technologies

Collaborative filtering


Content-based filtering


Find the closest customers and recommend what they buy
See what a customer has bought in the past, and use this
information to predict what he would like in the future. e.g.
Recommendation things that are similar to the things he bought
before.
Rule-based approach

Identify business rules about what products should be
recommended
Example:
IF a customer fits a certain profile (e.g. male, age 25-35), THEN recommend a
certain set of products.

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Agenda

Personalization

K-Nearest Neighbors

Collaborative Filtering

Case Discussion

Software Demo
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Nearest Neighbor Approaches

Based on the concept of similarity
 Memory-Based
Reasoning (MBR)

k Nearest Neighbor (KNN)

Collaborative Filtering (CF)
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K Nearest Neighbor (KNN)
K-Nearest Neighbor can be used for classification/prediction tasks.
Step 1: Using a chosen distance metric, compute the distance between
the new example and all past examples.
Step 2: Choose the k past examples that are closest to the new
example.
Step 3: Work out the predominant class of those k nearest neighbors the predominant class is your prediction for the new example. i.e.
classification is done by majority vote of the k nearest neighbors. For
prediction problem with numeric target variable, the (weighted)
average of the k nearest neighbors is used as the predicted target
value.
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How do we determine our neighbors?
-Distance Measure Revisited.

Each example is represented with a set of numerical
attributes
John:
Age=35
Income=95K
No. of credit cards=3

Rachel:
Age=41
Income=215K
No. of credit cards=2
“Closeness” is defined in terms of the Euclidean
distance between two examples.

The Euclidean distance between X=(x1, x2, x3,…xn) and Y
=(y1,y2, y3,…yn) is defined as:
D( X , Y ) 
n
2
(
x

y
)
 i i
i 1

Distance (John, Rachel)=sqrt [(35-41)2+(95K-215K)2 +(3-2)2]
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K-Nearest Neighbor Classifier
Example : 3-Nearest Neighbors
Customer Age Income No. credit cards Response
John
35
35K
3
No
Rachel
22
50K
2
Yes
Hannah
63
200K
1
No
Tom
59
170K
1
No
Nellie
25
40K
4
Yes
David
37
50K
2
?
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K-Nearest Neighbor Classifier
Example
Customer Age Income No.
Response Distance from
David
(K)
cards
sqrt [(35-37)2+(35-50)2
John
35 35
3
No
+(3-2)2]=15.16
Rachel
22 50
2
Yes
sqrt [(22-37)2+(50-50)2
+(2-2)2]=15
Hannah
63 200
1
No
sqrt [(63-37)2+(200-50)2
+(1-2)2]=152.23
Tom
59 170
1
No
sqrt [(59-37)2+(170-50)2
+(1-2)2]=122
Nellie
25 40
4
Yes
sqrt [(25-37)2+(40-50)2
+(4-2)2]=15.74
David
37 50
2
Yes (2/3)
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Some Issues with Euclidian Distance

Scaling of values


Weighting of attributes:



Since each numeric attribute may be measured in different units,
they should be standardized.
Manual weighting: Weights may be suggested by experts
Automatic weighting: Weights may be computed based on
discriminatory power or other statistics. (e.g. in SAS, weighted
dimension is based on the correlation to the target variable.)
Treatment of categorical variables

Various ways of assigning distance between categories are
possible.
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Dealing with Categorical Values
• For categorical values, we can to convert them to numeric values.
• We might treat ‘being in class A’ as ‘1’, and ‘not in class A’ as 0.
Therefore, two items in the same class have distance 0 for that
attribute, and two items in different classes have distance 1 for that
attribute. For example:
Take the bridge attributes: (deck type, purpose)
Take the bridges:
Bridge 1 = (concrete, auto)
Bridge 2 = (steel, railway)
Bridge 3 = (concrete, railway)
We could compute distances as:
d(Bridge1,Bridge2) = 1 + 1 = 2
d(Bridge2,Bridge3) = 1 + 0 = 1
d(Bridge1,Bridge3) = 0 + 1 = 1
• Again, some form of weighting for attributes of different importance
may be useful.
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Dealing with Categorical Values
• We might also construct aggregation hierarchies, so that categories
far away from each other conceptually are given higher distances.
Deck
Concrete deck
Pre-cast deck
Steel deck
Cast-at-site deck
• Using this hierarchy, we might regard the distance between pre-cast
and cast-at-site as 1 (they have a common parent), while the distance
between pre-cast and steel could be 2 (they have a common
grandparent). The distance between concrete and steel would be 1
(they have a common parent).
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How to Decided K?
Assume a new example X (at the center of the circles below). Notice
that:
• With a 3-Nearest Neighbor classifier (inner circle), X is assigned
to the majority Class B, whereas
• With an 11-Nearest Neighbor classifier (outer circle), X is
assigned to the majority Class A.
• Can use validation data set to decide k.
Attribute B
Class A
Class B
X
Attribute A
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Strengths of K-Nearest Neighbor
• Often work well for classes that are hard to separate using parametric
methods or the splits used by decision trees.
• Simple to implement and use
• Comprehensible – easy to explain prediction
• Robust to noisy data by averaging k-nearest neighbors.
• Some appealing applications (e.g. personalization)
Attribute B
Class A
Class B
Class C
Class D
Attribute A
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Problems with K Nearest Neighbor (KNN)
• How to choose k ? Do we use 1 nearest neighbor, 10
nearest neighbors, 50 nearest neighbors?
• Computational cost: For a large database, we’d have to
compute the distance between the new example and
every old example, and then sort by distance, which can
be very time-consuming. Possible resolutions are:
• sampling: store only a sample of the historic data so
that you have fewer distances to compute.
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Applications of MBR
• Medicine / 911: Find which diagnosis was made for similar
symptoms in the past, and adapt treatment appropriately
• Customer Support (HelpDesk): Find which solution was proposed
for similar problems in the past, and adapt appropriately (e.g.
Compaq’s SMART/QUICKSOURCE system)
• Engineering / Construction: Find what costing or design was made
for projects with similar requirements in the past, and adapt
appropriately
• Law (Legal Advice): Find what judgment was made for similar cases
in the past, and adapt appropriately
• Audit and Consulting Engagements: find similar past projects
• Insurance Claims Settlement: find similar claims in the past
• Real estate: Property price appraisal based on previous sales
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Agenda

Personalization

K-Nearest Neighbors

Collaborative Filtering

Case Discussion

Software Demo
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Collaborative Filtering:
Finding the like-minded people

One seeks recommendations about movies,
restaurants, books etc. from people with
similar tastes

Automate the process of "word-of-mouth"
by which people recommend products or
services to one another.

CF is a variant of MBR particularly well
suited to personalized recommendations
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Collaborative Filtering
Starts with a history of people’s personal
preferences
 Uses a distance function – people who
like the same things are “close”
 Uses “votes” which are weighted by
distances, so close neighbor votes count
more

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Collaborative filtering
Consumers’ preferences are registered
1.
Restaurants Rating (0:bad - 10:Excelent)
Fridays Thai Food The Barns University Cafe Cosi
Don
5
1
6
6
2
Rachel
1
4
2
3
5
David
1
3
2
???
???
…
2.
3.
4.
David is seeking recommendations on restaurants .
Using a similarity metric, the similarity between another person
and David is calculated based on their preferences (i.e.,
restaurant ratings).
Their (weighted) average ratings for any given restaurant is
computed, and restaurants with a high average score are
recommended to David.
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Collaborative filtering
1.
Distance


2.
Weighted Score


3.
David and Don:
sqrt[(5-1)2+(1-3)2+(6-2)2]=6
David and Rachel: sqrt[(1-1)2+(4-3)2+(2-2)2]=1
6*(1/7) + 3*(6/7) = 3.4
2*(1/7) + 5*(6/7) = 4.6
Ranking

Cosi > University Cafe
Restaurants Rating (0:bad - 5:Excelent)
Fridays Thai Food The Barns University Cafe Cosi
Don
5
1
6
6
2
Rachel
1
4
2
3
5
David
1
3
2
???
???
…
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Collaborative Filtering: Drawback for sellers
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Need real time recommendation
Scale – millions of customers, thousands of items
Works well only once a "critical mass" of preference has
been obtained
Need a very large number of consumers to express their
preferences about a relatively large number of products.
Consumer input is difficult to get
Solution: identify preferences that are implicit in people's actions
 For example, people who order a book implicitly express their
preference for the book they buy over other books
 Works well but results are not as good as the results achieved
using explicit ratings.

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
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Example:
Implicit rating
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Case Discussion

Firefly
1.
2.

Polyphonic HMI
1.
2.
3.

What are the pros and cons of collaborative filtering, contentbased systems and rule-based systems for recommendations?
What industries/applications is each technique good for?
How are implicit ratings learned? What are the limitations of
implicit ratings? How can they be improved?
How is the same technique used for music recommendation
and hit-song prediction?
How can Hit Song Science benefit the record labels, producers
and the unsigned artists?
Can KNN be used for hit-song prediction? How?
General Discussion Question:
1.
How to evaluate a personalization system?
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