Is593-Lecture04 Recommendation Systems

7/30/2019 Is593-Lecture04 Recommendation Systems

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King Saud University

College of Computer & Information Sciences

IS 593 Selected Topics in E-Commerce

Lecture 4Recommendation Systems

Dr. Mourad YKHLEF

The slides content is derived and adapted from many references


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Content

Introduction Collaborative Filtering

Content-based Filtering Other directions


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Definition

A system that predicts a users rating or

preference to an item.

Help people discover interesting or

informative stuff that they wouldn't have

thought to search for.


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Recommender Systems

E-Commerce: Amazon.com; eBay; Levis, Ski-

europe.com.

News: Digg; GroupLens.

File: Credence.

Movie: Nettfilx.com; Moviefinder.com.


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Recommender Algorithms

We category the existing recommender algorithms

into:

Content-based Recommendation;

Collaborative Filtering Recommendation; Heuristic-based or Memory-based; Model-based.

Knowledge-based Recommendation;

Hybrid Recommendation.


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Content




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What is collaborative filtering (CF) ?

The most popular technique for recommender system

Making filtering decisions for an individual userbased on thejudgments of other users

General Idea

Given an active user u, find friends {u1, , um}

Recommend new items to the active user based on the

opinions of his/her friends


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Collaborative Filtering Matrix8

Jack Oliver Susan Bob

Item 1 5 2 4

Item 2 4 2 2

Item 3 4 2

Item 4 2 4

Item 5 5 2 4

2

4

3.5

1.5


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CF : Intuitions9

User similarity

Suppose Jamie and I liked similar items in the past sixmonths

If Jamie liked Item 4, I will also like it

Item similarity

Since 90% of those who liked Item 4 also liked

Item 7, and, you liked Item 4

You may also like Item 7


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Short History of CF10

1992 GroupLens Project : (Usernet News) 1994

1995 Ringo: music (later Firefly, purchased by Microsoft?) Bellcore: Video Recommender

1996 Recommender Systems Workshop in SIGIR

After 1996 substantial integration with machine learning, information

filtering

Increasing commercial application After 2000

Web recommender system


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Commercial applications amazon.com11

Input: One artist/author name


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Commercial applications Amazon.com12

Output: List of Recommendations

Explore / Refine Recommendations

Search usingRecommendations


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Commercial applications Sleeper13

Input: Ratings of 10 books for all users

Use of continuous Rating Bar


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Collaborative

Filtering

Non-probabilistic

Algorithms

ProbabilisticAlgorithms

User-basednearestneighbor

Item-based

nearestneighbor

Reducing

dimensionality

Bayesian-network models

EM algorithm


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MUser-based collaborative filteringAproaches15

General Ideas for user-based

Find top K friends for active user a based on usersimilarity- Sim (a, i)

Make prediction for the active user based on the ratings ofthose top K friends, Pre(a,i) = f( ratings of K users, i)

Specific approaches differ in

Sim(a,i) -- the distance/similarity between two users

Such as Pearson correlation coefficient and Cosine similarity

Many other possibilities

Pre(a,i) = f( ratings of K users, i)


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Step 1 : Find K nearest friends by Pearson Coefficient

F Algorithm

16

k,i k L,i L

i N

kL2

2k L

k,i k L,i Li N

k,i L,i

k

(R -R )(R -R )cov(k,L)

W = =

(R -R ) (R -R )

Where, N is the number of items co-rated by user K and user L;

R and R is the user K and L's rating on item i , respectively;

R and

LR is the average ratings of user k and L , respectively.


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Step 2 : Predict users rating on item

17

i , m k , i

i f r i e n d s

k , m

k , i

i f r i e n d s

i , m i k , i

i f r i e n d s

k , m k

k , i

i f r i e n d s

k , m

k

k , i

R W

P r e =W

( R - R ) W

P r e = R +W

w h e r e R i s U s e r k 's r a t i n g s o n i t e m m ;

R i s t h e a v e r a g e r a t i n g s o f u s e r K ;

W i s t h e r e l a t i o n s h i p b e t w e e n u s e

o r

r k a n d i .


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18

Pearson correlation coefficient

ken

Lee

meg

k,i k L,i Li N

ken-lee kL2

2k Lk,i k L,i L

i N

2 2 2 2

(1 5 2 4)

R 34

(4 2 5 1)R 3

4

(2 4 3)R 3

3

(R -R )(R -R )cov(k,L)W =W = =

(R -R ) (R -R )

(1 3) (4 3) (5 3) (2 3) (2 3) (5 3) (4 3) (1 3)

(1 3) (5 3) (2 3) (4 3) (4

+ + += =

+ + += =

+ += =

+ + + =

+ + +

2 2 2 23) (2 3) (5 3) (1 3)

0.8

+ + +

=

User

Items Ken Lee Meg Nan

1 1 4 2

2 5 2 4

3 3 5

4 2 5

5 4 1

6 ? 2 5 ?

So use the same method we can get Wken-meg=+1.0 . We do not needcalculate Wken-nan ,because Nan do not gave mark for Item six.


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Pearson CF Algorithm

19

Predict Kens preference on Item 6

UserItem Ken Lee Meg Nan

1 1 4 2

2 5 2 4

3 3 5

4 2 5

5 4 1

6 ? 2 5 ?

i,m i k ,i

i frien d s

, 6 k ,m k k ,i

i frien ds

(R -R )W

P r R = R +W

(5 3) (2 3) 2 ( 0 .8 )3 3 4 .5 6

1 0 .8

K en

ken m eg ken lee

ken m eg ken lee

e

W W

W W

=

+ = + = + =

+ +


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Problem of Pearson CF20

Data sparseness

Misleading friends

User

Item Ken Lee Meg Nan

1 1 52 2

3 3

4 5

5 4

6 ? 2 ?

User

Item Ken Lee Meg Nan

1 1 1 12 5 5 5

3

4 2 1

5 4 3

6 ? 1 5 5


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21


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Item-based collaborative filtering

Basic idea:

Use the similarity between items (and not users) to make predictions

Example:

Look for items that are similar to Item5

Take Alice's ratings for these items to predict the rating for Item5

Item1 Item2 Item3 Item4 Item5

Alice 5 3 4 4 ?

User1 3 1 2 3 3

User2 4 3 4 3 5

User3 3 3 1 5 4

User4 1 5 5 2 1


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The cosine similarity measure


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Making predictions

A common prediction function:

Neighborhood size is typically also limited to a specific size

Not all neighbors are taken into account for the prediction

An analysis of the MovieLens dataset indicates that "in most real-world

situations, a neighborhood of 20 to 50 neighbors seems reasonable"

(Herlocker et al. 2002)


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Another example for

user-based and

item-based


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26

UserUser--User recommendationsUser recommendations

Users who bought X like Y.Users who bought X like Y.

Each user is represented by a vectorvector

indicating his ratings for each productratings for each product. Users with a small distancesmall distancebetween

each other are similarsimilar.

Find a similar user and recommendthings they like that you havent rated.


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27

Step 1: Represent input dataStep 1: Represent input data

u1 u2 u3 u4 u5 u6

item1 5 1 5 4 0 3

item2 3 3 1 1 5 1item3 0 1 ? 2 1 4

item4 1 1 4 1 1 2

item5 3 2 5 0 0 3item6 4 3 0 0 4 0

item7 0 1 5 1 1 1

Ranking matrix


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28

Step 2.1. Calculate similarity betweenvectors

Cosine formula

Pearson correlation

Step 2: Find nearest neighboursStep 2: Find nearest neighbours


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UserUser--User Collaborative filteringUser Collaborative filtering

u1 u2 u3 u4 u5 u6

item1

5 1 5 4 0 3

item2 3 3 1 1 5 1

item3 0 1 ? 2 1 4

item4 1 1 4 1 1 2item5 3 2 5 0 0 3

item6 4 3 0 0 4 0

item7 0 1 5 1 1 10.630.63 0.760.76 0.710.71 0.220.22 0.930.93Similarity measureSimilarity measure

0.63 = 5*5+3*1+1*4+3*5+4*0+0*5 / sqrt(5*5 + 3*3+1 +9+16 )*sqrt(25+1+16+25+25)= 47/(sqrt(60)*sqrt(92))


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ItemItem--Item Collaborative filteringItem Collaborative filtering

u1 u2 u3 u4 u5 U6item1 5 1 5 4 0 3

item2 3 3 1 1 5 1

item3 0 1 ? 2 1 4item4 1 1 4 1 1 2

item5 3 2 5 0 0 3

item6 4 3 0 0 4 0

item7 0 1 5 1 1 1

Similarity measure

0.62

0.9

0.850.85

0.64

0.230.23

0.44


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Step 2: Find nearest neighboursStep 2: Find nearest neighbours

Step 2.2. Define neighbourhood (sizeStep 2.2. Define neighbourhood (size LL))

sort and take first L

aggregate neighbourhood(at each step take

the closest to the centroid)


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u1 u2 u3 u4 u5 U6

item1 5 1 5 4 0 3

item2 3 3 1 1 5 1

item3 0 1 ? 2 1 4item4 1 1 4 1 1 2

item5 3 2 5 0 0 3

item6 4 3 0 0 4 0

item7 0 1 5 1 1 10.630.63 0.760.76 0.710.71 0.220.22 0.930.93Similarity measure

2.5

Weighted sum

(1x0.76+2x0.71+4x0.93)

(0.76+0.71+0.93)

= 2.5


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33

u1 u2 u3 u4 u5 U6

item1 5 1 5 4 0 3

item2 3 3 1 1 5 1

item3 0 1 ? 2 1 4item4 1 1 4 1 1 2

item5 3 2 5 0 0 3

item6 4 3 0 0 4 0

item7 0 1 5 1 1 1

Similarity measureSimilarity measure

0.620.62

0.90.9

0.850.85

0.640.64

0.230.23

0.440.44

4.6

Weighted sum

(4x0.9+5x0.85+5x0.64)

(0.9+0.85+0.64)

= 4.6


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Lora Aroyo 34

Step 3: Generate RecommendationsStep 3: Generate Recommendations

Most frequent items scans the neighbourhoodscans the neighbourhood andcalculates the frequency for each item can be combined with the rating value

Association rules recommendation expands the number of items based on

association rules upon what has beenrecommended by the neighboursrecommended by the neighbours


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Lora Aroyo 35

ItemItem--Item: ChallengesItem: Challenges

Getting the users to tell you what they likeGetting the users to tell you what they like Both financial and time reasons not to.

Getting enough data to make novel predictionsGetting enough data to make novel predictions.

What users really want are recommendations for thingstheyre not aware of.

Most effective when you have metadatametadata that lets youautomatically relate itemsautomatically relate items.

Genre, actors, director, etc. Also best when decoupled from paymentdecoupled from payment

Users should have an incentive to rate items truthfully.


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Lora Aroyo 36

UserUser--User RecommendationsUser Recommendations

Advantages: Users dont need to rate much. No info about products needed.

Easy to implement

Disadvantages: Pushes users toward the middle products with

more ratings carry more weight.

How to deal with new products? Many products and few users -> lots of things dont

get recommended.


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Netflix Prize Contest


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Model Based - Probabilistic methods


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Calculation of probabilities in simplistic approach

Item1 Item2 Item3 Item4 Item5

Alice 1 3 3 2 ?

User1 2 4 2 2 4

User2 1 3 3 5 1

User3 4 5 2 3 3

User4 1 1 5 2 1

More to consider

Zeros (smoothing required)

like/dislike simplification possible

X = (Item1 =1, Item2=3, Item3= 3, item4=2)

of Alice


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Content




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Content-Based Recommending

Recommendations are based on information on

the content of items rather than on other usersopinions.

Uses machine learning algorithms to induce a

profile of the users preferences from examplesbased on a featural description of content.

Lots of systems


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Advantages of Content-Based Approach

No need for data on other users.

No cold-start or sparsity problems.

Able to recommend to users with unique tastes.

Able to recommend new and unpopular items

No first-rater problem.

Can provide explanations of recommended items bylisting content-features that caused an item to be

recommended.

Well-known technology The entire field ofClassification Learning is at (y)our disposal!


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You detail


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Disadvantages of Content-Based Method

Requires content that can be encoded as meaningful

features. Users tastes must be represented as a learnable

function of these content features.

Unable to exploit quality judgments of other users.

Unless these are somehow included in the content

features.


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Content



Temporal Dynamics in the


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p yRecommendations

Item-side effects: Product perception and popularity are constantly

changing Seasonal patterns influence items popularity

User-side effects:

Customers ever redefine their taste Transient, short-term bias; anchoring

Drifting rating scale

Change of rater within household

Time Sensiti e Recommenders


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Time Sensitive Recommenders

Koren (2009)

Collaborative Filtering with Temporal Dynamics

He use factor models to separate differentaspects of the ratings to observe changes in:

1. Rating scale of individual users

2. Popularity of individual items3. User preferences


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Extending Capabilities of Recommender Systems

Comprehensive understanding of users and items

Multidimensionality of Recommendations: add additionalcontextual information to the User X Item space

Multcriteria Ratings: find Pareto optimal solutions, take alinear combination of multiple criteria, optimize the mostimportant criterion, consecutively optimize one criterion ata time


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Social Recommender System

Motivation: Social Overload

Information Overload

Interaction Overload

Target the social media domain

Aim at coping with the challenge of social overload

Aim at increasing adoption and engagement

Often apply personalization techniques

Utilize social network and content, incorporate short-term

interest and long-term interest, Accuracy vs. Serendipitytradeoff


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Social Recommender System

Tag Recommendation

People Recommendation

Community Recommendation

Recommendation for groups

Recommenders in Enterprise

Recommenders in Activity Stream

Problems

Cold start

Trust and distrust (Reputation), explanaition

Temporal Aspects in Social Recommendation


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Mobile Recommender System

Offer personalized, context-sensitive recommendations

Models Context-Dependent Recommendations Distributed Models, e.g. P2P

Proactive Recommendations

Difficulties: Data is more complex

Transplantation problemrecommendations may not apply in allregions

Documents

Is593-Lecture04 Recommendation Systems