This document discusses data mining techniques and recommendation systems. It describes common data mining techniques like classification, clustering, regression, association rule mining and outlier analysis. It also discusses the knowledge discovery process and applications of data mining. The document then covers recommendation systems, describing content-based, collaborative filtering and hybrid recommendation approaches. It provides examples of these systems.

1. Data Mining and
Recommendation
Systems
- S A L IL NAVG IR E

2. Introduction
• Discovery of models for data
• Example if the data is set of numbers then we
assume that the data comes from Gaussian and
model the parameters to define it completely
• Recognize meaningful patterns in data -> data
mining
Predict outcome from known patterns -> ML

3. Data Mining Techniques
• Classification
• Predicting the class of new item given set of items with
several classes and past instances
• Example loan approval based on decision tree classifiers
Job
Engineer
Carpenter
Income
<30K
Bad
>50K
Good
Income
<40K
Bad
>90K
Good
Doctor
Income
>100K
<50K
Bad
Good

4. • Clustering
• Clustering algorithms find group of items that are similar
• Basically divides a dataset so that records with similar
content are in the same group and group are as different as
possible from each other
• K-Nearest Neighbor – a classification method that clasifies
based on calculating the distances between point and
other points in the training dataset
• Example Car Sales

5. • Regression
• Deals with prediction of value rather than class
• Given x1, x2, x3….. Predict Y
• Use Linear regression and predict variables a0, a1, a2… in
Y=a0+a1x1+a2x2…..
• Use Line fitting, Curve fitting methods
• Example find a relationship between smoking patients and
cancer related illness

6. • Association Rules
• These algorithms create rules that describe how often
events have occurred together
• Example when a customer buys a hammer then 90% of the
time they buy nails
• Spam classification based on conditional probability
• Support is a measure of what fraction of the population
satisfies both the antecedent and the consequent of the
rule
• Confidence is the measure of how often the consequent is
true when the antecedent is true
• Outlier Analysis
• Most Data mining methods discard outliers as noise or
exceptions
• However in some applications such as fraud detection,
these rare events can be more interesting

7. Knowledge Discovery Process
• Data Collection
• Data Cleaning
• Data Integration
• Data selection
• Data transformation
• Data Mining
• Evaluation
• Knowledge presentation

8. Applications of Data Mining
• Marketing
• Manufacturing
• Analysis of consumer behavior
• Optimization of resources
• Advertising campaigns
• Optimization of manufacturing
processes
• Targeted mailings
• Segmentation of customers,
stores, or products
• Finance
• Product design based on
customer requirements
• Health Care
• Creditworthiness of clients
• Discovering patterns in X-ray
images
• Performance analysis of finance
investments
• Analyzing side effects of drugs
• Fraud detection
• Effectiveness of treatments

9. Privacy Concerns
• Effective Data Mining requires large sources of data
• To achieve a wide spectrum of data, link multiple data
sources
• Linking sources leads can be problematic for privacy as
follows: If the following histories of a customer were
linked:
• Shopping History
• Credit History
• Bank History
• Employment History
• The users life story can be painted from the collected data

10. Recommendation systems
• Definition – RS are subclass of information filtering
systems that seek to predict the rating or preference
that user would give to an item
• Enhance user experience by assisting user in finding
information and reduce search and navigation time
• Increase productivity and credibility
• Decrease Long tail phenomenon
• Types of RS
• Content based RS
• Collaborative filtering RS
• Hybrid RS

11. • Content based RS
•
Recommend items similar to those users preferred in
the past
•
User profiling is the key
•
Items/content usually denoted by keywords
• Limitations
• Not all contents well represented by keywords (e.g Images)
• unrated items not shown
• Users with thousands of purchases is a problem
• Example: Pandora uses properties of a song in the Music
Genome Project to play similar songs

12. • Collaborative Filtering method
• Uses other users rating for recommendation
• Key is to find users/user groups whose interests match with the
current user
• More users, more ratings: better results
• Limitations
• Cold Start problem
• Large computation power required
• Sparsity
• Example: Last.fm or Spotify recommend songs based on
user listening history and comparing with other users.
Facebook, LinkedIn use collaborative filtering to
recommend new friends and connections

13. • Hybrid RS
• There are some cases where combining content based and
collaborative filtering are more effective
• Can overcome the sparsity and cold start problem
• Netflix Prize: offered a prize of 1 million to team that could
increase the Netflix rating by 10%. The competition
spanned from 2006-2009 won by BellKor's Pragmatic
Chaos who used ensemble of 107 algorithms for single
prediction!
• Amazon item to item collaboration
• Compute similarity between item pairs
• Combine the similar items into recommendation list
• Vector corresponds to an item, and directions correspond
to customers who have purchased them
• Similar items table built offline

14. • Measuring similarity

15. Examples
• E-Commerce: Amazon.com, Ebay, Etsy.
• Music: Spotify, Pandora.
• Movie: Nettfilx.com, IMDB.
• News: Digg, Summly.
• Social Networks: LinkedIn, Facebook, Quora, YouTube
• Apps: Playstore, Cover