RECOMMENDER SYSTEMS AND SEARCH ENGINES – TWO SIDES OF THE SAME
COIN?!Bracha Shapira
Lior Rokach
Department of Information Systems Engineering
Ben-Gurion University
CONTENT
Introduction Applications Methods Recommender Systems vs. search engines
ARE YOU BEING SERVED? What are you looking for? Demographic – Age, Gender, etc. Context-
Casual/Event Season Gift
Purchase History Loyal Customer What is the customer currently wearing?
Style Color
Social Friends and Family Companion
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RECOMMENDER SYSTEMS
A recommender system (RS) 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
EXAMPLE APPLICATIONS
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WHAT BOOK SHOULD I BUY?
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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.
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In October 2006, Netflix announced it would give a $1 million to whoever created 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's recommendation software
After a month, more than a thousand programs had been entered, and the top scorers were almost halfway to the goal
But what started out looking simple suddenly got hard. The rate of improvement began to slow. The same three or four teams clogged the top of the leader-board.
Progress was almost imperceptible, and people began to say a 10 percent improvement might not be possible.
Three years later, on 21st of September 2009, Netflix announced the winner.
abcdThe Nextflix prize story
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WHAT NEWS SHOULD I READ?
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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.
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Usage in the market/products Recommendation State-of-the-art solutions
Method CommonnessExamined Solutions
Jinni Taste Kid Nanocrowd Clerkdogs Criticker IMDb Flixster Movielens Netflix Shazam Pandora LastFM YooChoose Think Analytics Itunes Amazon
Collaborative Filtering v v v v v v v v v v v v
Content-Based Techniques v v v v v v v v v v v
Knowledge-Based Techniques v v v v v v v Stereotype-Based Recommender Systems
v v v v v v v
Ontologies and Semantic Web Technologies for Recommender Systems
v v v
Hybrid Techniques v v v v v v v
Ensemble Techniques for Improving Recommendation
v future
Context Dependent Recommender Systems
v v v v v v
Conversational/Critiquing Recommender Systems
v v
Community Based Recommender Systems and Recommender Systems 2.0
v v v v v
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COLLABORATIVE FILTERING
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kNN - Nearest Neighbor SVD – Matrix Factorization
Similarity Weights Optimization (SWO)
The method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of CF approach is that those who agreed in the past tend to agree again in the future.
Collaborative Filtering1Des
crip
tion
Sel
ecte
d Te
chni
ques
Collaborative Filtering
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COLLABORATIVE FILTERING
Trying to predict the opinion the user will have on the different items and be able to recommend the “best” items to each user based on: the user’s previous likings and the opinions of other like minded users
abcdThe Idea
?Positive Rating
Negative Rating
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abcdHow it works
How collaborative filtering works?“People who liked this also liked”…
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Item to
Item
User to User
abcdUser-to-User
Recommendations are made by finding users with similar tastes. Jane and Tim both liked Item 2 and disliked Item 3; it seems they might have similar taste, which suggests that in general Jane agrees with Tim. This makes Item 1 a good recommendation for Tim.This approach does not scale well for millions of users.
Item-to-Item
Recommendations are made by finding items that have similar appeal to many users. Tom and Sandra are two users who liked both Item 1 and Item 4. That suggests that, in general, people who liked Item 4 will also like item 1, so Item 1 will be recommended to Tim. This approach is scalable to millions of users and millions of items.
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Hamming distance
5 6 6 5 4 8
0 Dislike
1 Like
? Unknown
1
?
0
1
1
0
1
1
0
1
1
1
1
0
Current User Users
Item
s
User Model = interaction history
1
1st item rate
14th item rate
KNN - NEAREST NEIGHBOR
NearestNeighbor
Nearest Neighbor
abcd
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This user did not rate the item. We will try to predict a rating according to hisneighbors.
This user did not rate the item. We will try to predict a rating according to his neighbors.
abcdUnknown Rating
There are other users who rated the same item. We are interested in the Nearest Neighbors.
There are other users who rated the same item. We are interested in the Nearest Neighbors.
abcdOther Users
We are looking for the Nearest Neighbor. The one with the lowest Hamming distance.
We are looking for the Nearest Neighbor. The one with the lowest Hamming distance.
abcdNearest Neighbors
The prediction was made based on the nearest neighbor.
The prediction was made based on the nearest neighbor.
abcdPrediction
The Hamming distance is named after Richard Hamming.
In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different.
The Hamming distance is named after Richard Hamming.
In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different.
abcdHamming Distance
IMPORTANT ISSUES
Cold Start Implicit/Explicit Rating Sparsity
Long Tail problem - many items in the Long Tail have only few ratings
Portfolio Effect: Non Diversity Problem It is not useful to recommend all movies by Antonio
Banderas to a user who liked one of them in the past Beyond Popularity
Gray sheep problem Iformation Security
Misuse Privacy
CONTENT-BASED RECOMMENDER SYSTEM
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CONTENT-BASED RECOMMENDATION In content-based recommendations the system
tries to recommend items that matches the User Profile.
The Profile is based on items user has liked in the past or explicit interests that he defines.
A content-based recommender system matches the profile of the item to the user profile to decide on its relevancy to the user.
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SIMPLE EXAMPLE
Read updat
e User Profile
New books User Profile
Recommender Systems
Match
recommendation
CONTEXT-BASED RECOMMENDER SYSTEMS
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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
abcdOverview
Context-Based Recommender Systems
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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.
Motivating Examples
Context-Based Recommender Systems
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Recommend music The music that we like to hear is greatly affected by a
context, such that can be thought of a mixture of our feelings (mood) and the situation or location (the theme) we associate it with.
Listen to Bruce Springteen "Born in USA" while driving along the 101.
Listening to Mozart's Magic Flute while walking in Salzburg.
Motivating Examples
Context-Based Recommender Systems
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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"
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Context-Based Recommender Systems
What is the user doing when asking for a recommendation? Where (and when) the user is located? What does the user really want (e.g., improve his knowledge or
really buy a product)? Is the user alone or with other fellows? Are there many products to choose or only few?
What simple recommendation techniques ignore?
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What is the user doing when asking for a recommendation? Where (and when) the user is located? What does the user really want (e.g., improve his knowledge or
really buy a product)? Is the user alone or with other fellows? Are there many products to choose or only few?
Plain recommendation technologies forget to takeinto account the user context.
Context-Based Recommender Systems
What simple recommendation techniques ignore?
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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
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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 maximizesan impurity measure and whose impurity is higher than a threshold
abcdItem Split - Intuition and Approach
Context-Based Recommender Systems
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SOCIAL (TRUST) BASED RECOMMENDER SYSTEMS
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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
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?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
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3
TRUST METRICS (CONT.) Local metrics: predicts (different) trust scores
that are personalized from the point of view of every single user
Pros: More accurate Attack resistance
Cons: Ignoring the “wisdom of the crowd”
a
b
d
c
1 5
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SEARCH ENGINES AND RECOMMENDER SYSTEMS
SEARCH ENGINES VS. RECOMMENDER SYSTEMS –
Search Engines Goal – answer users
ad hoc queries Input – user ad-hoc
need defined as a query
Output- ranked items relevant to user need (based on her preferences???)
Methods - Mainly IR based methods
Recommender Systems Goal – recommend
services or items to user Input - user preferences
defined as a profile
Output - ranked items based on her preferences
Methods – variety of methods, IR, ML, UM
NEW TRENDS…
“Understand” the user actual needs from her context
Personalize results according to the user preferences
Search engines may use some recommender systems methods to achieve these goals
SEARCH ENGINES PERSONALIZATION METHODSADOPTED FROM RECOMMENDER SYSTEMS
Collaborative filtering User-based - Cross domain collaborative filtering is
required??? Content-based
Search history Collaborative content-based
Collaborate on similar queries Context-based
Little research – difficult to evaluate Locality, language, calendar
Social-based Friends I trust relating to the query domain Notion of trust, expertise
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