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TRANSCRIPT
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?
Types of Recommender System
Types of Recommender System
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
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).
Collaborative Filtering
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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.
Context-Based Recommender Systems…..
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
Context-Based Recommender Systems…..
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
Context-Based Recommender Systems…..
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!