topics covered - cdgi.edu.in systems.pdf · recommender systems a recommender system helps people...

Topics Covered

An Overview of Recommender System

Types of Recommender System

Case Study of Recommender System

Recent Research Areas and Challenges

Conclusion

An Overview of Recommender System

RECOMMENDER SYSTEMS

A Recommender system helps people that have

not sufficient personal experience or competence to

evaluate the, potentially overwhelming, number of

alternatives offered by a Web site.

– In their simplest form RSs recommend to their users

personalized and ranked lists of items.

– Provide consumers with information to help them

decide which items to purchase.

WHAT BOOK SHOULD I BUY?

WHAT MOVIE SHOULD I WATCH?

• The Internet Movie Database (IMDb) provides information about actors, films, television shows, television stars, video games and production crew personnel.

• Owned by Amazon.com since 1998 • 796,328 titles and 2,127,371 people• More than 50M users per month.

WHAT NEWS SHOULD I READ?

WHERE SHOULD I SPEND MY VACATION?

Tripadvisor.com

I would like to escape from this ugly an tedious work life and

relax for two weeks in a sunny place. I am fed up with

these crowded and noisy places … just the sand and the

sea … and some “adventure”.I would like to bring my wife and my children on a

holiday … it should not be to expensive. I prefer

mountainous places… not too far from home.

Children parks, easy paths and good cuisine are a

must.I want to experience the contact with a completely different

culture. I would like to be fascinated by the people and

learn to look at my life in a totally different way.

WHICH FRIENDS SHOULD I MAKE?


Content based Recommender System

Collaborative Filtering based Recommender System

Context based Recommender System

Trust based Recommender System

Other variations of Recommender System

Content based Recommender System

User Profiles

A profile of the user’s interests is used by most

recommendation systems

This profile consists of two main types of

information:

– A model of the user’s preferences. E.g., a function that for

any item predicts the likelihood that the user is interested in

that item.

– User’s interaction history. E.g., items viewed by a user,

items purchased by a user , search queries etc.

User Profiles…..

User’s history will be used as training data for a

machine learning algorithm that creates a user model

“Manual” recommending approaches

– User customization

• Provide “check box” interface that let the users construct

their own profiles of interests

• A simple database matching process is used to find items

that meet the specified criteria and recommend these to us

ers.

Decision Trees and Rule Induction

Well-suited for structured data

In unstructured data, the number of attributes

becomes too enormous and consequently, the tree

becomes too large to provide sufficient performance

RIPPER: a rule induction algorithm based on the

same principles but provide better performance in

classifying text

Nearest Neighbor Methods

Simply store all the training data in memory

To classify a new item, compare it to all stored items

using a similarity function and determine the “nearest

neighbor” or the k nearest neighbors.

The class or numeric score of the previously unseen

item can then be derived from the class of the nearest

neighbor.

Limitations

Can only be effective in limited circumstances.

It is not straightforward to recognize the subtleties incontent.

Depend entirely on previous selected items and thereforecannot make predictions about future interests of users.

These shortcomings can be addressed by collaborativefiltering (CF) techniques.

Collaborative Filtering


Maintain a database of many users’ ratings of a variety of

items.

For a given user, find other similar users whose ratings

strongly correlate with the current user.

Recommend items rated highly by these similar users, but not

rated by the current user.

Almost all existing commercial recommenders use this

approach (e.g. Amazon).


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RecommendationsC

Collaborative Filtering Method

Weight all users with respect to similarity with theactive user.

Select a subset of the users (neighbors) to use aspredictors.

Normalize ratings and compute a prediction from aweighted combination of the selected neighbors’ratings.

Present items with highest predicted ratings asrecommendations.

Similarity Weighting

Typically use Pearson correlation coefficient between ratings

for active user, a, and another user, u.

ua rr

uaua

rrc

),(covar,

ra and ru are the ratings vectors for the m items rated by

both a and u

ri,j is user i’s rating for item j

Rating Prediction

Predict a rating, pa,i, for each item i, for active user, a, by using

the n selected neighbor users, u {1,2,…n}.

To account for users different ratings levels, base predictions on

differences from a user’s average rating.

Weight users’ ratings contribution by their similarity to the active

user.

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Problems with Collaborative Filtering

Cold Start: There needs to be enough other users already inthe system to find a match.

Sparsity: If there are many items to be recommended, even ifthere are many users, the user/ratings matrix is sparse, and it ishard to find users that have rated the same items.

First Rater: Cannot recommend an item that has not beenpreviously rated.

– New items

– Esoteric items

Popularity Bias: Cannot recommend items to someone withunique tastes.

Context based Recommender System

The recommender system uses additional data about the

context of an item consumption.

For example, in the case of a restaurant the time or the

location may be used to improve the recommendation

compared to what could be performed without this additional

source of information.

A restaurant recommendation for a Saturday evening when

you go with your spouse should be different than a restaurant

recommendation on a workday afternoon when you go with

co-workers

Context-Based Recommender Systems

26

Recommend a vacation

Winter vs. summer

Recommend a purchase (e-retailer)

Gift vs. for yourself

Recommend a movie

To a student who wants to watch it on Saturday

night with his girlfriend in a movie theater.

Examples

Context-Based Recommender Systems…..

27

abcdMusicovery.com

An Interactive personalized WebRadio

A mood matrix propose a relationship between music and mood.

Ethnographic studies have shown that people choose music peaces according to their mood or mood change expectation.

abcdDetails

Information Discovery: Example“Tell me the music that I want to listen NOW"

28

What is the user when asking for a recommendation?

Where (and when) the user is ?

What does the user (e.g., improve his knowledge or really

buy a product)?

Is the user or with other ?

Are there products to choose or only ?

Plain recommendation technologies forget to takeinto account the user context.


What simple recommendation techniques ignore?

29

Obtain sufficient and reliable data describing the user context

Selecting the right information, i.e., relevant in a particular

personalization task

Understand the impact of contextual dimensions on the personalization

process

Computational model the contextual dimension in a more classical

recommendation technology

For instance: how to extend Collaborative Filtering to include

contextual dimensions?

abcdMajor obstacle for contextual computing


30

Each item in the data base ( ) is a candidate for splitting

Context defines ( ) all possible splits of an item ratings vector

We test all the possible splits – we do not have many contextual

features

We choose one split (using a single contextual feature) that maximizes

an impurity measure and whose impurity is higher than a threshold

abcdItem Split - Intuition and Approach


31

Trust based Recommender System

Intuition – Users tend to receive advice from people they

trust, i.e., from their friends.

Trusted friends can be defined explicitly by the users or

inferred from social networks they are registered to.

.

abcdOverview

Social Based (Trust based) Recommender Systems

33

?3

Active user

Rating

prediction

TRUST- BASED COLLABORATIVE FILTERING

Active users’ trusted friends

TRUST METRICS

Global metrics: computes a single global trust value

for every single user (reputation on the network)

Pros:

– Based on the whole community opinion

Cons:

– Trust is subjective (controversial users)

a

b

d

c

1 3

32

3

TRUST METRICS…..

Local metrics: predicts (different) trust scores that arepersonalized from the point of view of every singleuser

Pros:– More accurate

– Attack resistance

Cons:– Ignoring the “wisdom of the crowd”

a

b

d

c

1 5

32

?

Case Study of Recommender System

Live Demonstration of Hybrid Movie

Recommendation Engine

Recent Research Areas and Challenges

16.08.2014

In October 2006, Netflix announced it would give a $1 million to whoevercreated a movie-recommending algorithm 10% better than its own.

Within two weeks, the DVD rental company had received 169 submissions,including three that were slightly superior to Cinematch, Netflix'srecommendation software

After a month, more than a thousand programs had been entered, and the topscorers were almost halfway to the goal

But what started out looking simple suddenly got hard. The rate ofimprovement began to slow. The same three or four teams clogged the topof the leader-board.

Progress was almost imperceptible, and people began to say a 10 percentimprovement might not be possible.

Three years later, on 21st of September 2009, Netflix announced the winner.

abcdThe Nextflix prize story

39

Recent Research Areas in RS

Recommender System in Multimedia and E-Commerce i.e.

Movie, Music Recommender System

Automatically Sharing Web Experiences through a Hyper

document Recommender System

Preference Learning in Recommender Systems

Modeling Preferences in a Distributed Recommender System

A Semantic Tag Recommendation System

Recent Research Areas in RS…..

Application of Dimensionality Reduction in Recommender System

DNS Infrastructure Recommendation System

Architecture of a Recommender System to Support

Collaboration in a Software Environment

A Recommender System for the DSpace Open Repository Platform

Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness

How can we improve RS quality and performance by usingdimensionality reduction techniques?

How can we design better interface for RS?

How can we design distributed RS to make them widelyavailable?

How can utilize clustering algorithms to improve scalability inRS?

Community Recommenders

Confidence and Explanation

Social Navigation

Reducing Rating Effort

RS for New Items and Users

Small Device Recommenders

Research Challenges in RS

Conclusion

Recommender Systems reduces Human effort by largeextent, by suggesting optimal recommendations.

There are various types of RS, selection is based on one’srequirements.

It can be applied on almost every domain i.e. medical,industrial, educational etc.

Still better recommendation algorithms need to be developed,to provide faster and optimal results.

Conclusion

Thanking You!

topics covered - cdgi.edu.in systems.pdf · recommender systems a recommender system helps people...

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