collaborative filtering rong jin dept. of computer science and engineering michigan state university

Collaborative FilteringCollaborative Filtering

Rong Jin

Dept. of Computer Science and Engineering

Michigan State University

3

Information FilteringInformation Filtering

• Basic filtering question: Will user Basic filtering question: Will user UU like item like item XX??

• Two different ways of answering itTwo different ways of answering it– Look at what Look at what UU likes likes

characterize characterize XX content-based filteringcontent-based filtering– Look at who likes Look at who likes XX

characterize characterize UU collaborative filteringcollaborative filtering

4

Collaborative FilteringCollaborative Filtering(Resnick et al., 1994)(Resnick et al., 1994)

Make recommendation decisions for a specific user Make recommendation decisions for a specific user based on the judgments of users with similar interests.based on the judgments of users with similar interests.

5

Collaborative FilteringCollaborative Filtering(Resnick et al., 1994)(Resnick et al., 1994)

Make recommendation decisions for a specific user Make recommendation decisions for a specific user based on the judgments of users with similar interests.based on the judgments of users with similar interests.

User 1 1 5 3 4 5

User 2 4 1 5 2 3

User 3 2 ? 3 5 4

6

A General StrategyA General Strategy(Resnick et al., 1994)(Resnick et al., 1994)

1.1. Identify the training users that share similar Identify the training users that share similar interests as the test user.interests as the test user.

2.2. Predict the ratings of the test user as the average of Predict the ratings of the test user as the average of ratings by the training users with similar interestsratings by the training users with similar interests

7

A General StrategyA General Strategy(Resnick et al., 1994)(Resnick et al., 1994)

User 1 1 5 3 4 5

User 2 4 1 5 2 3

User 3 2 ? 3 5 45

1.1. Identify the training users that share similar Identify the training users that share similar interests as the test user.interests as the test user.

2.2. Predict the ratings of the test user as the average of Predict the ratings of the test user as the average of ratings by the training users with similar interestsratings by the training users with similar interests

8

Important Problems in Important Problems in Collaborative Filtering Collaborative Filtering

• How to estimate users’ similarity if rating data How to estimate users’ similarity if rating data is sparse?is sparse?

– Most users only rate a few itemsMost users only rate a few items

• How to identify interests of a test user if he/she How to identify interests of a test user if he/she only provides ratings for a few items?only provides ratings for a few items?

– Most users are inpatient to rate many itemsMost users are inpatient to rate many items

• How to combine collaborative filtering with How to combine collaborative filtering with content filtering?content filtering?

– For movie ratings, both the content information For movie ratings, both the content information and the user ratings are available and the user ratings are available

Problem I:Problem I: How to Estimate Users’ Similarity How to Estimate Users’ Similarity

based on Sparse Rating Data?based on Sparse Rating Data?

10

Sparse Data ProblemSparse Data Problem

(Breese et al., 1998)(Breese et al., 1998)

User 1 ? 5 3 4 2

User 2 4 1 5 ? 5

User 3 5 ? 4 2 5

User 4 1 5 3 5 ?

Most users only rate a small number of items and Most users only rate a small number of items and leave most items unratedleave most items unrated

11

Flexible Mixture Model (FMM) Flexible Mixture Model (FMM) (Si & Jin, 2003)(Si & Jin, 2003)

• Cluster training users of similar interestsCluster training users of similar interests

User 1 ? 5 3 4 2

User 2 4 1 5 ? 5

User 3 5 ? 4 2 5

User 4 1 5 3 5 ?

12


• Cluster training users of similar interestsCluster training users of similar interests• Cluster items with similar ratingsCluster items with similar ratings

User 1 ? 5 3 4 2

User 2 4 1 5 ? 5

User 3 5 ? 4 2 5

User 4 1 5 3 5 ?

13


User Class I 1 p(4)=1/4

p(5)=3/4

3

User Class II p(4)=1/4

p(5)=3/4

p(1)=1/2

p(2)=1/2

p(4)=1/2

p(5)=1/2

Movie Type I

Movie Type II

Movie Type III

• Unknown ratings are gone!Unknown ratings are gone!

14


• Introduce rating uncertaintyIntroduce rating uncertainty

User Class I 1 p(4)=1/4

p(5)=3/4

3

User Class II p(4)=1/4

p(5)=3/4

p(1)=1/2

p(2)=1/2

p(4)=1/2

p(5)=1/2

Movie Type I

Movie Type II

Movie Type III

• Unknown ratings are gone!Unknown ratings are gone!

• Cluster both users and items simultaneouslyCluster both users and items simultaneously

15


Zo Zu

O U R

Zu: user class

Zo: item class

U: user

O: item

R: rating

Cluster variable

Observed variable( ) ( ) ( )

( ) ( ) ( ),

( , , )

( ) ( ) ( | ) ( | ) ( | , )o u

l l l

o u l o l u l o uz z

P o u r

P z P z P o z P u z P r z zAn Expectation Maximization (EM) algorithm can be used for identifying clustering structure for both users and items

16

Rating Variance Rating Variance (Jin et al., 2003a)(Jin et al., 2003a)

• The Flexible Mixture Model is based on the assumption The Flexible Mixture Model is based on the assumption that users of similar interests will have similar ratings for that users of similar interests will have similar ratings for the same itemsthe same items

• But, different users of similar interests may have different But, different users of similar interests may have different rating habitsrating habits

17




User 1 3 5 4 4 3

User 2 1 3 2 2 1

User 3 5 1 5 1 5

User 4 1 4 2 3 1

18




User 1 3 5 4 4 3

User 2 1 3 2 2 1

User 3 5 1 5 1 5

User 4 1 4 2 3 1

19

Hidden variableObserved variable

Decoupling Model (DM)Decoupling Model (DM)(Jin et al., 2003b)(Jin et al., 2003b)

Zo Zu

O U

Zu: user class

Zo: item classU: userO: itemR: rating

R

20

Decoupling Model (DM) Decoupling Model (DM) (Jin et al., 2003b)(Jin et al., 2003b)

Zpref : whether users like items

Zpref

Zo Zu

O U

Zu: user class


R


21



ZR: rating class

Zu: user class


ZR

Zpref

Zo Zu

O U R


22



ZR: rating class

Zu: user class


ZR

Zpref

Zo Zu

O U R

Hidden variableObserved variable( ) ( ) ( )

( ) ( ) ( )

( ), ,

( , , )

( | ) ( | ) ( ) ( | )

( | , ) ( | , )u R o pref

l l l

l u R l o l o

pref u o l R prefz z z z

P o r u

P u z P z u P z P x z

P z z z P r z z

23

Empirical StudiesEmpirical Studies

• EachMovie dataset:EachMovie dataset:– 2000 users and 1682 movie items2000 users and 1682 movie items– Avg. # of rated items per user is 130Avg. # of rated items per user is 130– Rating range: 0-5Rating range: 0-5

• Evaluation protocolEvaluation protocol– 400 training users, and 1600 testing users400 training users, and 1600 testing users

– Numbers of items rated by a test user: 5, 10, 20Numbers of items rated by a test user: 5, 10, 20

• Evaluation metric: MAEEvaluation metric: MAE• MAE: mean absolute error between true ratings and predicted MAE: mean absolute error between true ratings and predicted

ratingsratings• The smaller the MAE, the better the performanceThe smaller the MAE, the better the performance

24

Baseline ApproachesBaseline Approaches

• Ignore unknown ratingsIgnore unknown ratings– Vector similarity (Breese et al., 1998)Vector similarity (Breese et al., 1998)

• Fill out unknown ratings for individual users with Fill out unknown ratings for individual users with their average ratingstheir average ratings– Personal diagnosis (Pennock et al., 2000) Personal diagnosis (Pennock et al., 2000) – Pearson correlation coefficient (Resnick et al., 1994)Pearson correlation coefficient (Resnick et al., 1994)

• Only cluster usersOnly cluster users– Aspect model (Hofman & Puzicha, 1999)Aspect model (Hofman & Puzicha, 1999)

25

Experimental ResultsExperimental Results

0.9

1

1.1

1.2

1.3

1.4

5 items 10 items 20 Items

# of Rated Items from Test Users

MA

E

Pearson Correlation

Vector Similarity

Personal Diagnosis

Aspect Model

Flexible Mixture Model

Decoupling Model

26

SummarySummary

• The sparse data problem is important to The sparse data problem is important to collaborative filteringcollaborative filtering

• Flexible Mixture Model (FMM) is effectiveFlexible Mixture Model (FMM) is effective– Cluster both users and items simultaneouslyCluster both users and items simultaneously

• Decoupling Model (DM) provides additional Decoupling Model (DM) provides additional improvement for collaborative filteringimprovement for collaborative filtering– Take into account rating variance among users of Take into account rating variance among users of

similar interestssimilar interests

Problem II:Problem II:How to Identify Users’ Interests How to Identify Users’ Interests based on A Few Rated Items?based on A Few Rated Items?

28

Identify Users’ InterestsIdentify Users’ Interests• To identify the interests of a user, the system needs to To identify the interests of a user, the system needs to

ask the user to rate a few itemsask the user to rate a few items• Given a user is only willing to rate a few items, which Given a user is only willing to rate a few items, which

one should be asked to solicit rating?one should be asked to solicit rating?

29

Identify Users’ InterestsIdentify Users’ Interests

User 1 1 5 1 3 5

User 2 3 1 5 4 5

User 3 3 4 3 2 4

User 4 ? 5 ? ? ?

• To identify the interests of a user, the system needs to To identify the interests of a user, the system needs to ask the user to rate a few itemsask the user to rate a few items

• Given a user is only willing to rate a few items, which Given a user is only willing to rate a few items, which one should be asked to solicit rating?one should be asked to solicit rating?

30


User 1 1 5 1 3 5

User 2 3 1 5 4 5

User 3 3 4 3 2 4

User 4 ? 5 ? ? ?



31


User 1 1 5 1 3 5

User 2 3 1 5 4 5

User 3 3 4 3 2 4

User 4 ? 5 ? ? ?



32


User 1 1 5 1 3 5

User 2 3 1 5 4 5

User 3 3 4 3 2 4

User 4 ? 5 ? ? ?



33

Active Learning ApproachesActive Learning Approaches(Ross & Zemel, 2002(Ross & Zemel, 2002))

• Selective samplingSelective sampling– Ask a user to rate the items that are most Ask a user to rate the items that are most

distinguishable for users’ interestsdistinguishable for users’ interests

• A general strategyA general strategy– Define a loss function that represents the uncertainty in Define a loss function that represents the uncertainty in

determining users’ interestsdetermining users’ interests

– Choose the item whose rating will result in the largest Choose the item whose rating will result in the largest reduction in the loss functionreduction in the loss function

34

Active Learning Approach (I)Active Learning Approach (I)(Jin & Si, 2004)(Jin & Si, 2004)

• Select the items that have the largest variance in Select the items that have the largest variance in the ratings by the most similar usersthe ratings by the most similar users

User 1 1 5 1 3 5

User 2 3 1 5 4 5

User 3 3 4 3 2 4

User 4 ? 5 ? ? ?

35

Active Learning Approach (II) Active Learning Approach (II) (Jin & Si, 2004)(Jin & Si, 2004)

• Consider all the training users when selecting items• Weight training users by their similarities when computing the

“uncertainty” of items

User 1 1 5 1 3 5

User 2 5 1 5 4 5

User 3 2 4 3 2 4

User 4 ? 5 ? ? ?

36

A Bayesian Approach for A Bayesian Approach for Active Learning Active Learning (Jin & Si, 2004)(Jin & Si, 2004)

• Flexible Mixture ModelFlexible Mixture Model– Key is to determine the user class for a test userKey is to determine the user class for a test user

• LetLet D D be the ratings already provided by test be the ratings already provided by test user user yy– DD = {( = {(xx11, , rr11), …, (), …, (xxkk, , rrkk)})}

• Let Let be the distribution of user class for test be the distribution of user class for test user user y y estimated based on estimated based on DD = {= {zz = = pp((z|yz|y)|1)|1zz mm}}

37


• When the user class distribution When the user class distribution truetrue of the test of the test user is given, we will select the item user is given, we will select the item xx* that* that

| ,*

( | , )

arg min logtrue

z x rtruez truez

x X z p r x

x

θ

38



x,rx,r be the distribution of user class for test user be the distribution of user class for test user y y

estimated based on estimated based on D + D + ((xx,,rr))

| ,*

( | , )

arg min logtrue

z x rtruez truez

x X z p r x

x

θ

39



x,rx,r be the distribution of user class for test user be the distribution of user class for test user y y

estimated based on estimated based on D + D + ((xx,,rr))

– Take into account the uncertainty in rating predictionTake into account the uncertainty in rating prediction

| ,*

( | , )

arg min logtrue

z x rtruez truez

x X z p r x

x

θ

40


• But, in reality, we never know the true user class But, in reality, we never know the true user class distribution distribution truetrue of the test user of the test user

• Replace Replace truetrue with the distribution with the distribution pp((|D)|D)

| ,* ''

( | , ') ( '| )

arg min log z x rzz

x X z p r x p D

x

θ θ

Two types of uncertainties1. Uncertainty in user class distribution 2. Uncertainty in rating prediction

41

Computational IssuesComputational Issues

• Estimating Estimating pp((|D|D) is computationally expensive) is computationally expensive• Calculating the expectation is also expensiveCalculating the expectation is also expensive

| ,* ''

( | , ') ( '| )

arg min log z x rzz

x X z p r x p D

x

θ θ

42

Approximate Posterior Distribution Approximate Posterior Distribution (Jin & Si, 2004)(Jin & Si, 2004)

• Approximate Approximate pp((|D|D) by Laplacian approximation) by Laplacian approximation– Expand the log-likelihood function around its Expand the log-likelihood function around its

maximum point maximum point **

*

*1

*

*'

'

( | )log 1 log

( | )

( ) ( | )

( )

( | , )where 1

( | , ')

z

zz

z z

zzz

i i zz

i i i zz

p

p

p D

p r x z

p r x z

*

θ D

θ D

θ

43

Compute Expectation Compute Expectation (Jin & Si, 2004)(Jin & Si, 2004)

• Expectation can be computed analytically using Expectation can be computed analytically using the approximate posterior distribution the approximate posterior distribution pp((|D|D) )

''

| ,''

( | , ) ( | ( ))

*

* * *

| ,

* * * *

| ,

* *

log

( 1)( 2)

( | , ) ( | , )( 1)

( 1) ( | , ) log ( | , )

( 1) ( 1)

( |log ( | , )

( 1)

z x rzz

z p r x p D y

j jj

i ir i r i

j jj j r x ir j j i

r

ij j r xj

r

a aa

a p r x i a p r x ia a a

a a p r x j a a p r x i

a a a a

a p r xa p r x j

a a

* *

, )

( 1)r i

i

a a

44


• EachMovie datasetEachMovie dataset– 400 training users, and 1600 test users400 training users, and 1600 test users

• For each test userFor each test user– Initially provides 3 rated itemsInitially provides 3 rated items– 5 iterations, and 4 items are selected for each 5 iterations, and 4 items are selected for each

iterationiteration

• Evaluation metricEvaluation metric– Mean Absolute Error (MAE)Mean Absolute Error (MAE)

45

Baseline ApproachesBaseline Approaches

• The random selection methodThe random selection method– Randomly select 4 items for each iterationRandomly select 4 items for each iteration

• The The model entropymodel entropy method method– Select items that result in the largest reduction in the Select items that result in the largest reduction in the

entropy of distribution entropy of distribution pp((|D|D) ) – Only considers Only considers the uncertainty in model distributionthe uncertainty in model distribution

• The The prediction entropyprediction entropy method method– Select items that result in the largest reduction in the Select items that result in the largest reduction in the

uncertainty of rating predictionuncertainty of rating prediction– Only considers Only considers the uncertainty in rating predictionthe uncertainty in rating prediction

46

Experimental Results

1

1.05

1.1

1.15

1 2 3 4 5 6

Iterations

MA

E

random

Bayesian

Model Entropy

Prediction Entropy

47

SummarySummary

• Active learning is effective for identifying Active learning is effective for identifying users’ interestsusers’ interests

• It is important to take into account every bit It is important to take into account every bit of uncertainty when applying active of uncertainty when applying active learning methodslearning methods

Problem IIIProblem IIIHow to Combine Collaborative Filtering How to Combine Collaborative Filtering

with Content Filtering?with Content Filtering?

49

Collaborative Filtering + Content Info.Collaborative Filtering + Content Info.

User 1 User 2 User 3 Content

1 4 2 A high-school boy is given the chance to write a story about an up-and-coming rock band as he accompanies it on their concert tour.

5 1 5 A homicide detective and a fire marshall must stop a pair of murderers who commit videotaped crimes to become media darlings

3 5 3 A biography of sports legend, Muhammad Ali, from his early days to his days in the ring

4 2 5 A young adventurer named Milo Thatch joins an intrepid group of explorers to find the mysterious lost continent of Atlantis.

5 5 4 Benjamin Martin is drawn into the American revolutionary war against his will when a brutal British commander kills his son.

50


User 1 User 2 User 3 Content Information



3 5 3 A biography of sports legend, Muhammad Ali, from his early days to his days in the ring



51

Linear Combination Linear Combination (Good et al., 1999)(Good et al., 1999)

• Build a different prediction model for Build a different prediction model for content information and collaborative content information and collaborative informationinformation

• Linearly combine their predictions togetherLinearly combine their predictions together

52

User 1 User 2 User 3 Content Information Test user


1


4

pcf(r|x) pcont(r|x)+

( | ) ( | ) (1 ) ( | )comb cf contp r x p r x p r x

Linear Combination Linear Combination (Good et al., 1999)(Good et al., 1999)

53

The Co-Training ApproachThe Co-Training Approach(Hoi, Lyu & Jin, 2005)(Hoi, Lyu & Jin, 2005)

• The linear combination approach ignores the The linear combination approach ignores the correlation between content information and correlation between content information and collaborative informationcollaborative information

• We propose a Co-training approach for exploiting the We propose a Co-training approach for exploiting the correlation between these two types of informationcorrelation between these two types of information

54


User 1User 1 User 2User 2 User 3User 3 Content InformationContent Information TestTestuseruser

11 22 44 A high-school boy is given the chance to write A high-school boy is given the chance to write a story about an up-and-coming rock band as a story about an up-and-coming rock band as he accompanies it on their concert tourhe accompanies it on their concert tour..

11

55 11 55 A homicide detective and a fire marshall must A homicide detective and a fire marshall must stop a pair of murderers who commit stop a pair of murderers who commit videotaped crimes to become media darlingsvideotaped crimes to become media darlings

?? ?? 33 A biography of sports legend, Muhammad Ali, A biography of sports legend, Muhammad Ali, from his early days to his days in the ringfrom his early days to his days in the ring

44

44 22 55 A young adventurer named Milo Thatch joins A young adventurer named Milo Thatch joins an intrepid group of explorers to find the an intrepid group of explorers to find the mysterious lost continent of Atlantis.mysterious lost continent of Atlantis.

55 44 33 Benjamin Martin is drawn into the American Benjamin Martin is drawn into the American revolutionary war against his will when a brutal revolutionary war against his will when a brutal British commander kills his son.British commander kills his son.

55

55


User 1User 1 User 2User 2 User 3User 3 Content InformationContent Information TestTestUserUser

11 22 44 A high-school boy is given the chance to write A high-school boy is given the chance to write a story about an up-and-coming rock band as a story about an up-and-coming rock band as he accompanies it on their concert tourhe accompanies it on their concert tour..

11

55 11 55 A homicide detective and a fire marshall must A homicide detective and a fire marshall must stop a pair of murderers who commit stop a pair of murderers who commit videotaped crimes to become media darlingsvideotaped crimes to become media darlings

?? ?? 33 A biography of sports legend, Muhammad Ali, A biography of sports legend, Muhammad Ali, from his early days to his days in the ringfrom his early days to his days in the ring

44

44 22 55 A young adventurer named Milo Thatch joins A young adventurer named Milo Thatch joins an intrepid group of explorers to find the an intrepid group of explorers to find the mysterious lost continent of Atlantis.mysterious lost continent of Atlantis.

55 44 33 Benjamin Martin is drawn into the American Benjamin Martin is drawn into the American revolutionary war against his will when a brutal revolutionary war against his will when a brutal British commander kills his son.British commander kills his son.

55

56


User 1 User 2 User 3 Content Information TestUser


1


? ? 3 A biography of sports legend, Muhammad Ali, from his early days to his days in the ring

4



5

57




1


4


4



5

58




1


4


5



5

59




1


4


5


4


5

60

Coupled Support Vector MachineCoupled Support Vector Machine(Hoi, Lyu & Jin, 2005)(Hoi, Lyu & Jin, 2005)

Content info. Collaborative info.

Representation xi ri

Weights wi ui

Rated iterms

Unrated iterms

Require both the content information and collaborative

information to provide consistent prediction for rated items

61

Coupled Support Vector MachineCoupled Support Vector Machine(Hoi, Lyu & Jin, 2005)(Hoi, Lyu & Jin, 2005)

Require both the content information and collaborative

information to provide coherent prediction on unrated items

Content info. Collaborative info.

Representation xi ri

Weights wi ui

Rated iterms

Unrated iterms

62

Alternating Optimization Alternating Optimization (Hoi, Lyu & Jin, 2005)(Hoi, Lyu & Jin, 2005)

• Fix (Fix (uu, , bbuu) and estimate optimal () and estimate optimal (ww, , bbww))

• Fix (Fix (ww, , bbww) and estimate optimal () and estimate optimal (uu, , bbuu))

Quadratic programming

Quadratic programming

63

Alternating Optimization Alternating Optimization (Hoi, Lyu & Jin, 2005)(Hoi, Lyu & Jin, 2005)

• Fix (Fix (uu, , bbuu) and () and (ww, , bbww), estimate ratings ), estimate ratings YY’ for ’ for

the unrated items the unrated items

– It can be decomposed into a set of optimization It can be decomposed into a set of optimization problems involved in single variablesproblems involved in single variables

64


• DatasetDataset– Images in 20 categories of the COREL datasetImages in 20 categories of the COREL dataset– 100 images randomly selected from each category100 images randomly selected from each category– Totally 2000 imagesTotally 2000 images

• Content informationContent information– Image features: colors, edges, and textureImage features: colors, edges, and texture

• Collaborative informationCollaborative information– Log of relevance judgments in the historyLog of relevance judgments in the history– 150 user sessions, 20 images are judged for each session150 user sessions, 20 images are judged for each session

65

Evaluation MethodologyEvaluation Methodology

• Evaluation is based on online relevance feedbackEvaluation is based on online relevance feedback

1.1. A query image is randomly generatedA query image is randomly generated2.2. 20 images are retrieved by a content-based image retrieval 20 images are retrieved by a content-based image retrieval

(CBIR) system for the given query image(CBIR) system for the given query image3.3. A user is asked to judge the relevance of the 20 images to the A user is asked to judge the relevance of the 20 images to the

query imagequery image4.4. The CBIR system refines the given query using the feedback The CBIR system refines the given query using the feedback

information from the user, and returns a new set of imagesinformation from the user, and returns a new set of images5.5. The mean average precision of the top returned images is used The mean average precision of the top returned images is used

as the evaluation metricas the evaluation metric

66

Baseline MethodsBaseline Methods

• Euclidean distance (‘Euclidean’)Euclidean distance (‘Euclidean’)– Measure the similarity between images using the Measure the similarity between images using the

Euclidean distance in low-level image featuresEuclidean distance in low-level image features– Neither relevance feedback nor log information is usedNeither relevance feedback nor log information is used

• Relevance feedback by a support vector machine Relevance feedback by a support vector machine (‘RF-SVM’)(‘RF-SVM’)– Build a support vector machine (SVM) based on the Build a support vector machine (SVM) based on the

users’ feedbackusers’ feedback– Only utilizes relevance feedback informationOnly utilizes relevance feedback information

• Linear combination approach (‘LRF-2SVM’)Linear combination approach (‘LRF-2SVM’)– Build SVM models that are based on relevance feedback Build SVM models that are based on relevance feedback

information and log informationinformation and log information– Linearly combine their predictionsLinearly combine their predictions

67

Experimental Results

Coupled Support Vector Machine

68

SummarySummary

• Combining content information and Combining content information and collaborative filtering is important for collaborative filtering is important for predicting users’ interestspredicting users’ interests

• It is important to exploit the correlation It is important to exploit the correlation between content information and between content information and collaborative information.collaborative information.

ConclusionConclusion

70


• Collaborative judgments are extremely valuable Collaborative judgments are extremely valuable informationinformation– Provide alternative representation of items in Provide alternative representation of items in

addition to their contentaddition to their content– Are more related to human perception than content Are more related to human perception than content

informationinformation

• It is particularly useful It is particularly useful – When content information is not availableWhen content information is not available– When content information is difficult to analyzeWhen content information is difficult to analyze

• e.g., imagese.g., images

71


• Carefully designed learning algorithms are the Carefully designed learning algorithms are the key to exploit collaborative informationkey to exploit collaborative information– Sparse data & rating variance Sparse data & rating variance mixture models mixture models– Identify users’ interests Identify users’ interests active learning active learning– Exploit content information Exploit content information co-training co-training

72

Existing Challenges

• Large-sized data for collaborative filtering– Scalability– Large diversity in users’ interests– Large diversity in the content of items

• Mixed types of users’ feedback– Ratings, ranking, textual notations, …

• The privacy issue

73

AcknowledgementAcknowledgement

• Luo SiLuo Si

• Chengxiang ZhaiChengxiang Zhai

• Jamie CallanJamie Callan

• Alex G. HauptmannAlex G. Hauptmann

• Joyce Y. ChaiJoyce Y. Chai

• Steven C.H. HoiSteven C.H. Hoi

• Michael R. LyuMichael R. Lyu

74

ReferenceReference• Hoi, C.H., M. R. Lyu, and Hoi, C.H., M. R. Lyu, and R. JinR. Jin (2005), Integrating User Feedback Log into Relevance (2005), Integrating User Feedback Log into Relevance

Feedback by Coupled SVM for Content-Based Image Retrieval, in the 1st IEEE Feedback by Coupled SVM for Content-Based Image Retrieval, in the 1st IEEE International Workshop on Managing Data for Emerging Multimedia Applications International Workshop on Managing Data for Emerging Multimedia Applications (EMMA 2005) (invited paper)(EMMA 2005) (invited paper)

• Jin, R.Jin, R. and L. Si (2004), A Study of Methods for Normalizing User Ratings in and L. Si (2004), A Study of Methods for Normalizing User Ratings in Collaborative Filtering, in the Proceedings of The 27th Annual International ACM Collaborative Filtering, in the Proceedings of The 27th Annual International ACM SIGIR Conference (SIGIR 2004) Sheffield, UK.SIGIR Conference (SIGIR 2004) Sheffield, UK.

• Jin, R.Jin, R., J. Y. Chai, and L. Si (2004), An Automated Weighting Scheme for , J. Y. Chai, and L. Si (2004), An Automated Weighting Scheme for Collaborative Filtering, in the Proceedings of the 27th Annual International ACM SIGIR Collaborative Filtering, in the Proceedings of the 27th Annual International ACM SIGIR Conference (SIGIR 2004) Sheffield, UK. Conference (SIGIR 2004) Sheffield, UK.

• Jin, R.Jin, R. and L. Si (2004), A Bayesian Approach toward Active Learning for and L. Si (2004), A Bayesian Approach toward Active Learning for Collaborative Filtering, in the Proceedings of the 20th Conference on Uncertainty in Collaborative Filtering, in the Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (UAI 2004) Banff, Alberta, Canada.Artificial Intelligence (UAI 2004) Banff, Alberta, Canada.

• Jin, R.Jin, R., L. Si, C.X. Zhai, and J. Callan (2003), Collaborative Filtering with Decoupled , L. Si, C.X. Zhai, and J. Callan (2003), Collaborative Filtering with Decoupled Models for Preferences and Ratings, the Twelfth International Conference on Models for Preferences and Ratings, the Twelfth International Conference on Information and Knowledge Management (CIKM 2003), 2003Information and Knowledge Management (CIKM 2003), 2003

• L. Si, and L. Si, and R. JinR. Jin (2003) (2003),, Product Space Mixture Model for Collaborative Filtering, the Product Space Mixture Model for Collaborative Filtering, the Twentieth International Conference on Machine Learning (ICML 2003),Washington, Twentieth International Conference on Machine Learning (ICML 2003),Washington, DC USA.DC USA.

• JinJin, , R.R., L. Si and C.X. Zhai (2003), Preference-based Graphic Models for Collaborative , L. Si and C.X. Zhai (2003), Preference-based Graphic Models for Collaborative Filtering, the 19th Conference on Uncertainty in Artificial Intelligence (UAI 2003), Filtering, the 19th Conference on Uncertainty in Artificial Intelligence (UAI 2003), Acapulco,Mexico.Acapulco,Mexico.

75

ReferenceReference• Ross, D. A. and R. S. Zemel (2002). Multiple-cause Vector Quantization. In Advances in Ross, D. A. and R. S. Zemel (2002). Multiple-cause Vector Quantization. In Advances in

Neural Information Processing Systems 15. Neural Information Processing Systems 15.

• Good, N., J. Schafer, J. Konstan, S. Borchers, B. Sarwar, J. Herlocker and J. Riedl. Good, N., J. Schafer, J. Konstan, S. Borchers, B. Sarwar, J. Herlocker and J. Riedl. (1999). Combining collaborative filtering with personal agents for better (1999). Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the 16th National Conference on Artificial recommendations. In Proceedings of the 16th National Conference on Artificial Intelligence.Intelligence.

• Hofmann, T., & J. Puzicha (1999). Latent Class Models for Collaborative Filtering. In Hofmann, T., & J. Puzicha (1999). Latent Class Models for Collaborative Filtering. In the Proceedings of International Joint Conference on Artificial Intelligence.the Proceedings of International Joint Conference on Artificial Intelligence.

• Breese, J. S., D. Heckerman, C. Kadie (1998). Empirical Analysis of Predictive Breese, J. S., D. Heckerman, C. Kadie (1998). Empirical Analysis of Predictive Algorthms for Collaborative Filtering. In the Proceeding of the Fourteenth Conference Algorthms for Collaborative Filtering. In the Proceeding of the Fourteenth Conference on Uncertainty in Artificial Intelligenceon Uncertainty in Artificial Intelligence

• Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, & J. Riedl (1994) Grouplens: An Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, & J. Riedl (1994) Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In Proceeding of the ACM Open Architecture for Collaborative Filtering of Netnews. In Proceeding of the ACM 1994 Conference on Computer Supported Cooperative Work.1994 Conference on Computer Supported Cooperative Work.

• Pennock, D. M., E. Horvitz, S. Lawrence, & C.L. Giles (2000) Collaborative Filtering Pennock, D. M., E. Horvitz, S. Lawrence, & C.L. Giles (2000) Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach. In the by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach. In the Proceeding of the Sixteenth Conference on Uncertainty in Artificial Intelligence.Proceeding of the Sixteenth Conference on Uncertainty in Artificial Intelligence.

collaborative filtering rong jin dept. of computer science and engineering michigan state university

Documents

user ratings

test user

user u

similar ratings user

specific user

user applications

static user

users similarity