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Analysis of Recommendation Algorithms for E-Commerce

Analysis of Recommendation Algorithms for E-Commerce. Badrul M. Sarwar, George Karypis*, Joseph A. Konstan, and John T. Riedl GroupLens Research/*Army HPCRC Department of Computer Science and Engineering University of Minnesota. Talk Outline. Recommender Systems for E-Commerce

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Analysis of Recommendation Algorithms for E-Commerce

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  1. Analysis of Recommendation Algorithms for E-Commerce Badrul M. Sarwar, George Karypis*, Joseph A. Konstan, and John T. Riedl GroupLens Research/*Army HPCRC Department of Computer Science and Engineering University of Minnesota

  2. Talk Outline • Recommender Systems for E-Commerce • Quality and Performance Challenges • Synopsis of Recommendation Process • Experimental Setup • Result Highlights • Conclusion

  3. Recommender Systems • Problem • Information and commerce overload • Solution • Knowledge Discovery in Database (KDD) • Recommender Systems (RS) • Collaborative Filtering

  4. 3 Collaborative Filtering • Adds human judgement to the filtering process

  5. Collaborative Filtering (contd.) • Major Tasks • Representation of input data • Customer-product rating matrix • Neighborhood formation • Output • Prediction • Top-N Recommendation

  6. Challenges of RS • Sparsity • Enormous size of customer-product matrix • Affects neighborhood formation • Results in poor quality and reduced coverage • Scalability • Lots of customers and products • Affects neighborhood and output • Results in high response time

  7. Challenges of RS • Synonymy • Similar products treated differently • Increases sparsity, loss of transitivity • Results in poor quality

  8. Use of SVD for Collaborative Filtering 1. Low dimensional representation O(m+n) storage requirement k x n m x k 2. Direct Prediction “.” m x n m x m similarity • Top-N Recommendation • Prediction (CF algorithm) 3. Neighborhood Formation

  9. Experimental Setup • Data sets • MovieLens Data (www.movielens.umn.edu) • Size 943 x 1,682 • 100,000 ratings entry • Ratings are from 1-5 • Used for Prediction and Neighborhood experiments • E-Commerce Data • Size 6,502 x 23,554 • 97,045 purchase entry • Purchase entries are dollar amounts • Used for Neighborhood experiment • Train and Test Portions • Percentage of Training data, x

  10. Experimental Setup • Benchmark Systems • CF-Predict • CF-Recommend • Metrics • Prediction • Mean Absolute Error (MAE) • Top-N Recommendation • Recall and Precision • Combined score F1

  11. Results: Prediction Experiment

  12. Results: Neighborhood Formation • Movie Dataset

  13. Results: Neighborhood Formation • E-Commerce Dataset

  14. Conclusion • SVD results are promising • Provides better Recommendation for Movie data • Provides better Prediction for x<0.5 • Not as good for the E-Commerce data • We only tried upto 400 dimensions • SVD provides better online performance • SVD is capable of meeting RS challenges • Sparsity • Scalability • Synonymy

  15. Acknowledgements • National Science Foundation under grants IIS 9613960, IIS 9734442, IIS 9978717, CCR 9972519, EIA 9986042, ACI 9982274. • Army Research Office DAAG-55-98-1-0441, DOE ASCI program. Army High Performance Computing Research Center grant DAAH-04-95-C-0008 • Thanks to Netperceptions Inc. for additional support. • Thanks to Fingerhut Inc. for the EC dataset.

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