recommender system

Recommender System

How does it work ?

Group 4: Nguyen Dao Tan Bao Nguyen Thi Ngoc Phu Cao Dinh Qui Pham Huy Thanh

Instructed by Dr.Tim Reichert

2 Nguyen Dao Tan Bao Nguyen Thi Ngoc Phu Cao Dinh Qui Pham Huy Thanh

Outline

Introduction Collaborative filtering algorithms

User-based Item-based Similarity algorithms Apache Mahout demo

Case study – Amazon Demo – Music recommender system


Introduction

Source: http://www.cguru.info/information_technology_branch.htm

Technologies help people do many jobs ..

… and also in searching information


Go to web directories


Or use search engines


So what is the Problem ?

Find

Knew what you

need

Search

Knew the keywords ?RECOMMENDER

SYSTEM


Recommender system

Predict & produce the most relevant

recommendations to its audiences

based on their tastes.


Where’s it applied ?


Breaking news

Source: http://blogs.wsj.com/digits/2014/01/17/amazon-wants-to-ship-your-package-before-you-buy-it/

Amazon Wants to

Ship Your Package

Before You Buy It

What is the secret behind ?


Recommender Approaches

Item Hierarchy

Attribute-based recommendations

Collaborative filtering – User-user Similarity

Collaborative filtering – Item-item Similarity

Social + Interest Graph Based

Model Based



Item Hierarchy





Model Based


What is Collaborative filtering ?

Method of making automatic predictions

About the interests of a user by collecting preferences or taste information from many users.

?


User-based CF

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User-based CF

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• General Idea• Algorithm• K-Nearest Neighbors


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I'm gonna rent a film to watch with my boyfriend this week. Do you have any suggestion ?



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What kind of film does he like ?What kind of film does he like ?




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I don't know but his best friend really like insect collecting



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Maybe your boyfriend is similar to him. You guys can watch spiderman !?



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NOT IN OUR SCOPE


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I don't know but he really enjoys our last film, iron man



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Really, my boyfriend also likes it and his favourite one is the amazing Spiderman so maybe you guys can try it

Really, my boyfriend also likes it and his favourite one is the amazing Spiderman so maybe you guys can try it


General Idea

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Similar

Recommend


Algorithm

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… …

8 8 03

…


Algorithm

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… …

8 8 03

…5

5


Algorithm

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… …

8 8 03

…


K-Nearest Neighbors

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Neighborhood of most similar users is computed first

Only items known to those users are considered


K-Nearest Neighbors

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…

8 8 03


Item-Based CF

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• General Idea• Why we need ?• Algorithm


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Really, if you enjoy ironman then you should try ironman 2

Really, if you enjoy ironman then you should try ironman 2


General Idea

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Item-based recommendation is derived from how similar items are to items, instead of users to users.

Similar

Recommen

d


Why we need ?

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So MANY users !!!


1/25/2014Human is COMPLEX ?


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10 000 users like

Like

8 000 users like


Algorithm

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…

……

…

8 5 16

Check every item that has no preference

For each of them, calculate the similarity between it and every item that has preference

… …


Similarities

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• Pearson correlation• Euclidean distance• Tanimoto


Pearson Correlation

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A hypothesis that how tall you are effects your self esteem


Pearson Correlation

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The Pearson correlation is a number between –1 and 1

Measure of the strength of a linear association between two variables


Pearson Correlation

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• Doesn’t take into account the number of items in which two users’ preferences overlap


Pearson Correlation

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• Doesn’t take into account the number of items in which two users’ preferences overlap

• If two users overlap on only one item, no correlation can be computed


Tanimoto

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Ignore preference values entirely.

• It’s the ratio of the size of the intersection to the size of the union of their preferred items

• When two users’ items completely overlap, the result is 1.0

• When they have nothing in common, it’s 0.0


Tanimoto

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= AB / ( A + B - AB)


Tanimoto

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Only use while underlying data contains only Boolean preferences

Too much noise in preferences

Mahout Basic Demo

Nguyen Dao Tan Bao Nguyen Thi Ngoc Phu Cao Dinh Qui Pham Huy Thanh

What is Apache Mahout?

• Open Source from Apache• Mahout is a Java library

o Implementing Machine Learning techniques• Recommendation• Clustering• Classification


Why do we prefer Mahout ?

• Apache License• Good Community & Documentation

• Scalableo Based on Hadoop (not mandatory!)


•

Physical Storage(database, files …)

Data Model

Recommender

Application


Recommendation in Mahout

• Input: raw data (user preferences)• Output: Preference estimation• Step 1

o Mapping raw data into a DataModel Mahout-compliant

• Step 2o Tuning recommender components

• Similarity measure, neighborhood, …

• Step 3o Recommend


Recommendation Components

• Five Java interfaceso DataModel interface:

• MySQLJDBCDataModel, FileDataModel …o UserSimilarity interface

• Methods to calculate the degree of correlation between two users

o ItemSimilarity interface• Methods to calculate the degree of correlation between two

itemso UserNeighborhood interface

• Methods to define the concept of ‘neighborhood’o Recommender interface

• Methods to implement the recommendation step itself


Similarity Metrics

• SIMILARITY_COOCCURRENCE • SIMILARITY_LOGLIKELIHOOD • SIMILARITY_TANIMOTO_COEFFICIENT• SIMILARITY_CITY_BLOCK • SIMILARITY_COSINE• SIMILARITY_PEARSON_CORRELATION • SIMILARITY_EUCLIDEAN_DISTANCE


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CASE STUDY

“Much is made of what the likes of Facebook, Google and Apple know about users. Truth is, Amazon may know more. And the massive retailer proves it every day “ - JP Mangalindan, Writer [*]

References: http://tech.fortune.cnn.com/2012/07/30/amazon-5/


CASE STUDY

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• Amazon recommendation system is based on a number of simple elements:o what a user has bought in the past and recently viewedo which items a user has in virtual shopping carto items the user has rated and liked,o what other customers have viewed and purchased

• The retail giant's call this "item-to-item collaborative filtering“ • And used this algorithm to heavily customize the browsing experience

for returning customers

References: http://tech.fortune.cnn.com/2012/07/30/amazon-5/


• The recommendation system worked, and Amazon reported very successfullyo 29% sales increase to $12.83 billion during its 2nd fiscal quarter (as of July 26,

2012 )o Compare to $9.9 billion during the same time last year

• Amazon has integrated recommendations into nearly every part of the purchasing process from product discovery to checkout

• "Our mission is to delight our customers by allowing them to serendipitously discover great products.“ an Amazon spokesperson

CASE STUDY

1/25/2014References: http://tech.fortune.cnn.com/2012/07/30/amazon-5/


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CASE STUDY – Amazon recommendations services

References http://www.google.com/patents/US7921042

http://www.google.com/patents/US7921042



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CASE STUDY - Generation of Similar Items Table

References http://www.google.com/patents/US7921042 (Fig.1)http://www.google.com/patents/US7113917 (Fig.3,4)

The recommendation services components include:- a recommendation

process- and an off-line table

generation process- a similar items table






1/25/2014References http://www.google.com/patents/US7921042 (Fig.1)

http://www.google.com/patents/US7113917 (Fig.2)

CASE STUDY - Generation of Recommendation






1/25/2014References http://www.google.com/patents/US7921042 (Fig.1)

http://www.google.com/patents/US7113917 (Fig.5)







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References http://www.google.com/patents/US7921042 (Fig.1)http://www.google.com/patents/US7113917 (Fig.7)







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EVALUATION

Experimental Settings• Offline Experiments

- Performed by using a pre-collected data set of users choosing or rating items- Simulate the behavior of users that interact with a recommendation system.- Assume that the user behavior when the data was collected will be similar enough to the user

behavior when the recommender system is deployed,- Make reliable decisions based on the simulation.

• User Studies - conducted by recruiting a set of test subject, - and asking and observing them to perform several tasks requiring an interaction with the

recommendation system.- We can then check whether the recommendations are used, and whether people read different

stories with and without recommendations then ask them whether recommend were relevant

• Online Experiments - measuring the change in user behavior when interacting with different recommendation

systems.- if users of one system follow the recommendations more often, or if some utility gathered from

users of one system exceeds utility gathered from users of the other system, then we can conclude that one system is superior to the otherReferences: Microsoft research :Evaluating Recommendation Systems -

Guy Shani and Asela Gunawardana


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EVALUATION

References: Microsoft research :Evaluating Recommendation Systems - Guy Shani and Asela Gunawardana

Reliable conclusion1. Confidence and p-values2. Multiple tests

Measure Metrics3. User Preference & Prediction Accuracy: voting from user

o Root Mean Squared Error (RMSE)o Measuring Usage Prediction

4. Coverage: Item Space & User Space5. Novelty: recommendations for items that the user did not know about6. Utility: the recommendation engine can be judged by the revenue

that it generates for the website


References

• Personalized recommendations of items represented within a database http://www.google.com/patents/US7113917

• Computer processes for identifying related items and generating personalized item recommendations http://www.google.com/patents/US7921042

• Microsoft research :Evaluating Recommendation Systems - Guy Shani and Asela Gunawardana

• Amazon Recommendation – Industry report

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Summary

• Recommender Systems• User-based vs Item-based• Similarity metrics

o Depend on data to choose the most suitable

• Evaluation and challenges• Apache mahout

o A Java library implements machine learning techniques

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Mahout Music Recommend Demo

IF YOU LIKE BRITNEY, YOU WILL

LOVE ….


Architecture of Recommender

DemoFriendLikes.csv

DataModel

FacebookRecommender Recommend

er

FacebookRecommenderSOAP

Glassfish Java 6 EE Server

facebook-recommender-demo.war

SOAP


Q&A

THANK YOU!

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recommender system

Data & Analytics

evaluating recommendation systems

microsoft research

insect collecting

gonna rent

case study

guy shani

iron man

recommendation system