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Collaborative Filtering Rong Jin Dept. of Computer Science and Engineering Michigan State University Information Filtering Basic filtering question: Will user U like item X ? Two different ways of answering it Look at what U likes  characterize X  content-based filtering

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collaborative filtering

Collaborative Filtering

Rong Jin

Dept. of Computer Science and Engineering

Michigan State University

information filtering
Information Filtering
  • Basic filtering question: Will user U like item X?
  • Two different ways of answering it
    • Look at what U likes

 characterize Xcontent-based filtering

    • Look at who likes X

 characterize Ucollaborative filtering

collaborative filtering resnick et al 1994
Collaborative Filtering(Resnick et al., 1994)

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

collaborative filtering resnick et al 19944
Collaborative Filtering(Resnick et al., 1994)

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

a general strategy resnick et al 1994
A General Strategy(Resnick et al., 1994)
  • Identify the training users that share similar interests as the test user.
  • Predict the ratings of the test user as the average of ratings by the training users with similar interests
a general strategy resnick et al 19946
A General Strategy(Resnick et al., 1994)
  • Identify the training users that share similar interests as the test user.
  • Predict the ratings of the test user as the average of ratings by the training users with similar interests

5

important problems in collaborative filtering
Important Problems in Collaborative Filtering
  • How to estimate users’ similarity if rating data is sparse?
    • Most users only rate a few items
  • How to identify interests of a test user if he/she only provides ratings for a few items?
    • Most users are inpatient to rate many items
  • How to combine collaborative filtering with content filtering?
    • For movie ratings, both the content information and the user ratings are available
sparse data problem breese et al 1998
Sparse Data Problem(Breese et al., 1998)

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

flexible mixture model fmm si jin 2003
Flexible Mixture Model (FMM) (Si & Jin, 2003)
  • Cluster training users of similar interests
flexible mixture model fmm si jin 200311
Flexible Mixture Model (FMM) (Si & Jin, 2003)
  • Cluster training users of similar interests
  • Cluster items with similar ratings
flexible mixture model fmm si jin 200312

Movie Type I

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

Movie Type II

Movie Type III

  • Unknown ratings are gone!
flexible mixture model fmm si jin 200313

Movie Type I

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

Movie Type II

Movie Type III

  • Introduce rating uncertainty
  • Unknown ratings are gone!
  • Cluster both users and items simultaneously
flexible mixture model fmm si jin 200314

Zu: user class

Zo: item class

U: user

O: item

R: rating

Cluster variable

Observed variable

Zu

Zo

U

O

R

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

An Expectation Maximization (EM) algorithm can be used for identifying clustering structure for both users and items

rating variance jin et al 2003a
Rating Variance (Jin et al., 2003a)
  • The Flexible Mixture Model is based on the assumption that users of similar interests will have similar ratings for the same items
  • But, different users of similar interests may have different rating habits
rating variance jin et al 2003a16
Rating Variance (Jin et al., 2003a)
  • The Flexible Mixture Model is based on the assumption that users of similar interests will have similar ratings for the same items
  • But, different users of similar interests may have different rating habits
rating variance jin et al 2003a17
Rating Variance (Jin et al., 2003a)
  • The Flexible Mixture Model is based on the assumption that users of similar interests will have similar ratings for the same items
  • But, different users of similar interests may have different rating habits
decoupling model dm jin et al 2003b
Decoupling Model (DM)(Jin et al., 2003b)

Zu: user class

Zo: item class

U: user

O: item

R: rating

Zo

Zu

R

U

O

Hidden variable

Observed variable

decoupling model dm jin et al 2003b19
Decoupling Model (DM) (Jin et al., 2003b)

Zu: user class

Zo: item class

U: user

O: item

R: rating

Zo

Zu

Zpref

Zpref: whether users like items

R

U

O

Hidden variable

Observed variable

decoupling model dm jin et al 2003b20
Decoupling Model (DM) (Jin et al., 2003b)

Zu: user class

Zo: item class

U: user

O: item

R: rating

ZR

Zo

Zu

Zpref

Zpref: whether users like items

ZR: rating class

R

U

O

Hidden variable

Observed variable

decoupling model dm jin et al 2003b21
Decoupling Model (DM) (Jin et al., 2003b)

Zu: user class

Zo: item class

U: user

O: item

R: rating

ZR

Zo

Zu

Zpref

Zpref: whether users like items

ZR: rating class

R

U

O

Hidden variable

Observed variable

empirical studies
Empirical Studies
  • EachMovie dataset:
    • 2000 users and 1682 movie items
    • Avg. # of rated items per user is 130
    • Rating range: 0-5
  • Evaluation protocol
    • 400 training users, and 1600 testing users
    • Numbers of items rated by a test user: 5, 10, 20
  • Evaluation metric: MAE
    • MAE: mean absolute error between true ratings and predicted ratings
    • The smaller the MAE, the better the performance
baseline approaches
Baseline Approaches
  • Ignore unknown ratings
    • Vector similarity (Breese et al., 1998)
  • Fill out unknown ratings for individual users with their average ratings
    • Personal diagnosis (Pennock et al., 2000)
    • Pearson correlation coefficient (Resnick et al., 1994)
  • Only cluster users
    • Aspect model (Hofman & Puzicha, 1999)
summary
Summary
  • The sparse data problem is important to collaborative filtering
  • Flexible Mixture Model (FMM) is effective
    • Cluster both users and items simultaneously
  • Decoupling Model (DM) provides additional improvement for collaborative filtering
    • Take into account rating variance among users of similar interests
identify users interests
Identify Users’ Interests
  • To identify the interests of a user, the system needs to ask the user to rate a few items
  • Given a user is only willing to rate a few items, which one should be asked to solicit rating?
identify users interests28
Identify Users’ Interests
  • To identify the interests of a user, the system needs to ask the user to rate a few items
  • Given a user is only willing to rate a few items, which one should be asked to solicit rating?
identify users interests29
Identify Users’ Interests
  • To identify the interests of a user, the system needs to ask the user to rate a few items
  • Given a user is only willing to rate a few items, which one should be asked to solicit rating?
identify users interests30
Identify Users’ Interests
  • To identify the interests of a user, the system needs to ask the user to rate a few items
  • Given a user is only willing to rate a few items, which one should be asked to solicit rating?
identify users interests31
Identify Users’ Interests
  • To identify the interests of a user, the system needs to ask the user to rate a few items
  • Given a user is only willing to rate a few items, which one should be asked to solicit rating?
active learning approaches ross zemel 2002
Active Learning Approaches(Ross & Zemel, 2002)
  • Selective sampling
    • Ask a user to rate the items that are most distinguishable for users’ interests
  • A general strategy
    • Define a loss function that represents the uncertainty in determining users’ interests
    • Choose the item whose rating will result in the largest reduction in the loss function
active learning approach i jin si 2004
Active Learning Approach (I)(Jin & Si, 2004)
  • Select the items that have the largest variance in the ratings by the most similar users
active learning approach ii jin si 2004
Active Learning Approach (II) (Jin & Si, 2004)
  • Consider all the training users when selecting items
  • Weight training users by their similarities when computing the “uncertainty” of items
a bayesian approach for active learning jin si 2004
A Bayesian Approach for Active Learning (Jin & Si, 2004)
  • Flexible Mixture Model
    • Key is to determine the user class for a test user
  • Let D be the ratings already provided by test user y
    • D = {(x1, r1), …, (xk, rk)}
  • Let  be the distribution of user class for test user y estimated based on D
    •  = {z = p(z|y)|1z m}
a bayesian approach for active learning jin si 200436
A Bayesian Approach for Active Learning (Jin & Si, 2004)
  • When the user class distribution true of the test user is given, we will select the item x* that
a bayesian approach for active learning jin si 200437
A Bayesian Approach for Active Learning (Jin & Si, 2004)
  • When the user class distribution true of the test user is given, we will select the item x* that
    • x,r be the distribution of user class for test user y estimated based on D + (x,r)
a bayesian approach for active learning jin si 200438
A Bayesian Approach for Active Learning (Jin & Si, 2004)
  • When the user class distribution true of the test user is given, we will select the item x* that
    • x,r be the distribution of user class for test user y estimated based on D + (x,r)
    • Take into account the uncertainty in rating prediction
a bayesian approach for active learning jin si 200439

Two types of uncertainties

  • Uncertainty in user class distribution 
  • Uncertainty in rating prediction
A Bayesian Approach for Active Learning (Jin & Si, 2004)
  • But, in reality, we never know the true user class distribution trueof the test user
  • Replace true with the distribution p(|D)
computational issues
Computational Issues
  • Estimating p(|D) is computationally expensive
  • Calculating the expectation is also expensive
approximate posterior distribution jin si 2004
Approximate Posterior Distribution (Jin & Si, 2004)
  • Approximate p(|D) by Laplacian approximation
    • Expand the log-likelihood function around its maximum point *
compute expectation jin si 2004
Compute Expectation (Jin & Si, 2004)
  • Expectation can be computed analytically using the approximate posterior distribution p(|D)
empirical studies43
Empirical Studies
  • EachMovie dataset
    • 400 training users, and 1600 test users
  • For each test user
    • Initially provides 3 rated items
    • 5 iterations, and 4 items are selected for each iteration
  • Evaluation metric
    • Mean Absolute Error (MAE)
baseline approaches44
Baseline Approaches
  • The random selection method
    • Randomly select 4 items for each iteration
  • The model entropy method
    • Select items that result in the largest reduction in the entropy of distribution p(|D)
    • Only considers the uncertainty in model distribution
  • The prediction entropy method
    • Select items that result in the largest reduction in the uncertainty of rating prediction
    • Only considers the uncertainty in rating prediction
summary46
Summary
  • Active learning is effective for identifying users’ interests
  • It is important to take into account every bit of uncertainty when applying active learning methods
linear combination good et al 1999
Linear Combination (Good et al., 1999)
  • Build a different prediction model for content information and collaborative information
  • Linearly combine their predictions together
the co training approach hoi lyu jin 2005
The Co-Training Approach(Hoi, Lyu & Jin, 2005)
  • The linear combination approach ignores the correlation between content information and collaborative information
  • We propose a Co-training approach for exploiting the correlation between these two types of information
coupled support vector machine hoi lyu jin 2005

Require both the content information and collaborative information to provide consistent prediction for rated items

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

Rated iterms

Unrated iterms

coupled support vector machine hoi lyu jin 200560

Require both the content information and collaborative information to provide coherent prediction on unrated items

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

Rated iterms

Unrated iterms

alternating optimization hoi lyu jin 2005

Fix (w, bw) and estimate optimal (u, bu)

Alternating Optimization (Hoi, Lyu & Jin, 2005)
  • Fix (u, bu) and estimate optimal (w, bw)

Quadratic programming

alternating optimization hoi lyu jin 200562
Alternating Optimization (Hoi, Lyu & Jin, 2005)
  • Fix (u, bu) and (w, bw), estimate ratings Y’ for the unrated items
    • It can be decomposed into a set of optimization problems involved in single variables
empirical studies63
Empirical Studies
  • Dataset
    • Images in 20 categories of the COREL dataset
    • 100 images randomly selected from each category
    • Totally 2000 images
  • Content information
    • Image features: colors, edges, and texture
  • Collaborative information
    • Log of relevance judgments in the history
    • 150 user sessions, 20 images are judged for each session
evaluation methodology
Evaluation Methodology
  • Evaluation is based on online relevance feedback
  • A query image is randomly generated
  • 20 images are retrieved by a content-based image retrieval (CBIR) system for the given query image
  • A user is asked to judge the relevance of the 20 images to the query image
  • The CBIR system refines the given query using the feedback information from the user, and returns a new set of images
  • The mean average precision of the top returned images is used as the evaluation metric
baseline methods
Baseline Methods
  • Euclidean distance (‘Euclidean’)
    • Measure the similarity between images using the Euclidean distance in low-level image features
    • Neither relevance feedback nor log information is used
  • Relevance feedback by a support vector machine (‘RF-SVM’)
    • Build a support vector machine (SVM) based on the users’ feedback
    • Only utilizes relevance feedback information
  • Linear combination approach (‘LRF-2SVM’)
    • Build SVM models that are based on relevance feedback information and log information
    • Linearly combine their predictions
experimental results66
Experimental Results

Coupled Support Vector Machine

summary67
Summary
  • Combining content information and collaborative filtering is important for predicting users’ interests
  • It is important to exploit the correlation between content information and collaborative information.
conclusion69
Conclusion
  • Collaborative judgments are extremely valuable information
    • Provide alternative representation of items in addition to their content
    • Are more related to human perception than content information
  • It is particularly useful
    • When content information is not available
    • When content information is difficult to analyze
      • e.g., images
conclusion70
Conclusion
  • Carefully designed learning algorithms are the key to exploit collaborative information
    • Sparse data & rating variance  mixture models
    • Identify users’ interests  active learning
    • Exploit content information  co-training
existing challenges
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
acknowledgement
Acknowledgement
  • Luo Si
  • Chengxiang Zhai
  • Jamie Callan
  • Alex G. Hauptmann
  • Joyce Y. Chai
  • Steven C.H. Hoi
  • Michael R. Lyu
reference
Reference
  • Hoi, C.H., M. R. Lyu, and R. Jin (2005), Integrating User Feedback Log into Relevance Feedback by Coupled SVM for Content-Based Image Retrieval, in the 1st IEEE International Workshop on Managing Data for Emerging Multimedia Applications (EMMA 2005) (invited paper)
  • Jin, R. and L. Si (2004), A Study of Methods for Normalizing User Ratings in Collaborative Filtering, in the Proceedings of The 27th Annual International ACM SIGIR Conference (SIGIR 2004) Sheffield, UK.
  • Jin, R., J. Y. Chai, and L. Si (2004), An Automated Weighting Scheme for Collaborative Filtering, in the Proceedings of the 27th Annual International ACM SIGIR Conference (SIGIR 2004) Sheffield, UK.
  • Jin, R. and L. Si (2004), A Bayesian Approach toward Active Learning for Collaborative Filtering, in the Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (UAI 2004) Banff, Alberta, Canada.
  • Jin, R., L. Si, C.X. Zhai, and J. Callan (2003), Collaborative Filtering with Decoupled Models for Preferences and Ratings, the Twelfth International Conference on Information and Knowledge Management (CIKM 2003), 2003
  • L. Si, and R. Jin (2003), Product Space Mixture Model for Collaborative Filtering, the Twentieth International Conference on Machine Learning (ICML 2003),Washington, DC USA.
  • Jin, R., L. Si and C.X. Zhai (2003), Preference-based Graphic Models for Collaborative Filtering, the 19th Conference on Uncertainty in Artificial Intelligence (UAI 2003), Acapulco,Mexico.
reference74
Reference
  • Ross, D. A. and R. S. Zemel (2002). Multiple-cause Vector Quantization. In Advances in Neural Information Processing Systems 15.
  • Good, N., J. Schafer, J. Konstan, S. Borchers, B. Sarwar, J. Herlocker and J. Riedl. (1999). Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the 16th National Conference on Artificial Intelligence.
  • Hofmann, T., & J. Puzicha (1999). Latent Class Models for Collaborative Filtering. In the Proceedings of International Joint Conference on Artificial Intelligence.
  • Breese, J. S., D. Heckerman, C. Kadie (1998). Empirical Analysis of Predictive Algorthms for Collaborative Filtering. In the Proceeding of the Fourteenth Conference on Uncertainty in Artificial Intelligence
  • 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 1994 Conference on Computer Supported Cooperative Work.
  • 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 Proceeding of the Sixteenth Conference on Uncertainty in Artificial Intelligence.