Recommender Systems and Search engines – two sides of the

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Transcript Recommender Systems and Search engines – two sides of the

ECOMMENDER

YSTEMS AND

EARCH ENGINES

–

TWO SIDES OF THE SAME COIN

Bracha Shapira Lior Rokach Department of Information Systems Engineering Ben-Gurion University

C

ONTENT

 Introduction    Applications Methods Recommender Systems vs. search engines

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RE

Y

OU

B

EING

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ERVED

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     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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ECOMMENDER

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YSTEMS

  

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

XAMPLE APPLICATIONS

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HAT BOOK SHOULD

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BUY

?

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HAT 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.

abcd The Nextflix prize story

   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 21 st of September 2009, Netflix announced the winner.

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W

HAT NEWS SHOULD

I

READ

?

W HERE 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 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 culture. I would like to be fascinated by the people and learn to look at my life in a totally different way.

Usage in the market/products Recommendation State-of-the-art solutions

Method Collaborative Filtering Content-Based Techniques Knowledge-Based Techniques Stereotype-Based Recommender Systems Ontologies and Semantic Web Technologies for Recommender Systems Hybrid Techniques Ensemble Techniques for Improving Recommendation Context Dependent Recommender Systems Conversational/Critiquing Recommender Systems Community Based Recommender Systems and Recommender Systems 2.0

Commonness Examined Solutions

Jinni Taste Kid Nanocrowd Clerkdogs Criticker IMDb Flixster Movielens Netflix

v v v v v v v v v v v v v v v v v v v v v v

Shazam Pandora LastFM YooChoose Think Analytics Itunes Amazon

v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v future v v v v v v v v v

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C OLLABORATIVE F ILTERING

Collaborative Filtering

 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.

1 Collaborative Filtering kNN - Nearest Neighbor  SVD – Matrix Factorization  Similarity Weights Optimization (SWO)  24.04.2020

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OLLABORATIVE

F

ILTERING

abcd The Idea

 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 Negative Rating ?

Positive Rating

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How collaborative filtering works?

“People who liked this also liked…”

User to User

abcd How it works

Item to Item

abcd User-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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K

NN - N

EAREST

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EIGHBOR

Current User 1 1 st item rate 0 Dislike 1 ?

Like Unknown User Model = interaction history We are looking for the Nearest Neighbor. The one with the 0 1 1 1 1 lowest Hamming 0 distance.

0 1 1 0 1 1  This user did not The prediction rate the item. We was made based  will try to predict a rating according neighbor.

to his neighbors.

 There are other users who rated the same item. We are interested in the Nearest  Neighbors.

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.

14 th item rate Hamming distance 5 6 6 5

abcd

Nearest  Neighbor 4 8 24.04.2020

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

C ONTENT -B ASED R ECOMMENDER S YSTEM

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ONTENT

-B

ASED

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ECOMMENDATION

   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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IMPLE

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XAMPLE

Read update User Profile

New books Match User Profile Recommender Systems recommendation

C ONTEXT BASED RECOMMENDER SYSTEMS

Context-Based Recommender Systems

abcd Overview

 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 24.04.2020

Context-Based Recommender Systems

Motivating Examples

 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.

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Context-Based Recommender Systems

Motivating Examples

 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.

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

abcd Musicovery.com

abcd Details

   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.

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Context-Based Recommender Systems

What simple recommendation techniques ignore?

 What is the user when asking for a recommendation?

 Where (and when) the user is ?  What does the user a product)?

(e.g., improve his knowledge or really buy  Is the user  Are there or with other ?

products to choose or only ?

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Context-Based Recommender Systems

What simple recommendation techniques ignore?

 What is the user when asking for a recommendation?

 Where (and when) the user is ?  What does the user a product)?

(e.g., improve his knowledge or really buy  Is the user  Are there or with other ?

products to choose or only ?

Plain recommendation technologies forget to take into account the user context.

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Context-Based Recommender Systems

abcd Major obstacle for contextual computing

 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?

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Context-Based Recommender Systems

abcd Item Split - Intuition and Approach

 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 24.04.2020

S OCIAL ( TRUST ) BASED RECOMMENDER SYSTEMS

Social Based (Trust based) Recommender Systems

abcd Overview

 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.

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RUST

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BASED

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OLLABORATIVE

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ILTERING

Active users’ trusted friends

Active user

Rating prediction

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RUST METRICS

 Global metrics: computes a single global trust value for every single user (reputation on the network) 1 b 3  Pros:  Based on the whole community opinion a  Cons:  Trust is subjective (controversial users) 2 c 3 3 d

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RUST METRICS

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CONT

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   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 ” 1 b a ?

2 c 5 3 d

S EARCH ENGINES AND RECOMMENDER SYSTEMS

S EARCH E NGINES VS . R ECOMMENDER S YSTEMS –    

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

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EW 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

S EARCH E NGINES P ERSONALIZATION ADOPTED FROM RECOMMENDER SYSTEMS M ETHODS      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