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Appears in Proceedings of the SIGIR-2001 Workshop on Recommender Systems,New Orleans, LA, September 2001

Content-Boosted Collaborative Filtering

Prem Melville, Raymond J. Mooney and Ramadass NagarajanDepartment of Computer Sciences

University of Texas

Austin, TX 78712

ABSTRACT

1. INTRODUCTION

2. MOTIVATING EXAMPLE


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3. DOMAIN DESCRIPTION

3.1 EachMovie Dataset

3.2 Data Collection

4. SYSTEM DESCRIPTION

4.1 Pure Content-based Predictor


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Matrix

Sparse User

Ratings

Full User

Ratings

Matrix

EachMovie

Active User Ratings

Recommendations

Web Crawler IMDb

Collaborative

Filtering

Movie

Content

ContentbasedPredictor

Database

4.2 Pure Collaborative Filtering


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4.3 Content-Boosted Collaborative Filtering

4.3.1 Harmonic Mean Weighting

1.05

1.1

1.15

1.2

1.25

1.3

1.35

1.4

1.45

1.5

0 20 40 60 80 100 120 140 160

Mean

Abso

lute

Error

No. of training examples


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Algorithm0.9

1.0

1.1

MAE

Content

CF

Naive

CBCF

Algorithm0.60

0.62

0.64

0.66

0.68

0.70

ROC-4

Content

CF

Naive

CBCF

6. DISCUSSION

6.1 Overcoming Sparsity and the First-RaterProblem

6.2 Finding Better Neighbors

6.3 Making Better Predictions

6.4 Self Weighting

6.5 Naive Hybrid

6.6 Efficient Implementation


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7. IMPROVING CBCF

7.1 Improving the Content-based Predictor

7.2 Improving the CF Component

8. RELATED WORK


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9. CONCLUSIONS AND FUTURE WORK

Acknowledgments

10. REFERENCES


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cbcf-sigir-wkshp-01

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