1. Catalog Integration R. Agrawal, R. Srikant: WWW-10

2. Catalog Integration Problem Integrate products from new catalog into master catalog.

3. The Problem (cont.) After integration:

4. Desired Solution Automatically integrate products:  little or no effort on part of user.  domain independent. Problem size:  Million products  Thousands of categories

5. Model Product descriptions consist of words Products live in the leaf-level categories

6. Basic Algorithm Build classification model using product descriptions in master catalog. Use classification model to predict categories for products in the new catalog.

7. National Semiconductor Files

8. National Semiconductor Files with Categories

9. Accuracy on Pangea Data B2B Portal for electronic components:  1200 categories, 40K training documents.  500 categories with < 5 documents. Accuracy:  72% for top choice.  99.7% for top 5 choices.

10. Enhanced Algorithm: Intuition Use affinity information in the catalog to be integrated (new catalog):  Products in same category are similar.  Bias the classifier to incorporate this information. Accuracy boost depends on quality of new catalog:  Use tuning set to determine amount of bias.

11. Algorithm Extension of the Naive-Bayes classification to incorporate affinity information

12. Naive Bayes Classifier Pr(C i |d) = Pr(C i )Pr(d|C i )/Pr(d) //Baye’s Rule Pr(d): same for all categories (ignore) Pr(C i ) = #docs  C i / #total docs Pr(d|C i ) = P w  d Pr(w|C i ) –Words occur independently (unigram model) Pr(w|C i ) = (n(C i,w)+  ) / (n(C i )+  |V|) –Maximum likelihood estimate smoothed with the Lidstone’s law of succession

13. Enhanced Algorithm Pr(C i |d,S) //d existed in category S = Pr(C i,d,S) / Pr(d,S) –Pr(C i,d,S) = Pr(d,S) Pr(C i |d,S) = Pr(C i )Pr(S,d|C i ) / Pr(d,S) = Pr(C i )Pr(S|C i )Pr(d| C i ) / Pr(S,d) –Assuming d, S independent given C i = Pr(S)Pr(C i |S)Pr(d| C i ) / Pr(S,d) –Pr(S|C i ) Pr(C i ) = Pr(C i |S) Pr(S) = Pr(C i |S)Pr(d|C i ) / Pr(d|S) –Pr(S,d) = Pr(S)Pr(d|S) Same as NB except Pr(C i |S) instead of Pr(C i ) –Ignore Pr(d|S) as it is same for all classes

14. Computing Pr(C i |S) Pr(C i |S) = |C i |  (#docs in S predicted to be in C i ) w /  j  [1,n] |C j |  (#docs in S predicted to be in C j ) w |C i | = #docs in C i in the master catalog w determines weight of the new catalog –Use a tune set of documents in the new catalog for which the correct categorization in the master catalog is known –Choose one weight for the entire new catalog or different weights for different sections

15. Superiority of the Enhanced Algorithm Theorem: The highest possible accuracy achievable with the enhanced algorithm is no worse than what can be achieved with the basic algorithm. Catch: The optimum value of the weight for which enhanced achieves highest accuracy is data dependent. The tune set method attempts to select a good value for weight, but there is no guarantee of success.

16. Empirical Evaluation Start with a real catalog M Remove n products from M to form the new catalog N In the new catalog N –Assign f*n products to the same category as M –Assign the rest to other categories as per some distribution (but remember their true category) Accuracy: Fraction of products in N assigned to their true categories

17. Improvement in Accuracy (Pangea)

18. Improvement in Accuracy (Reuters)

19. Improvement in Accuracy (Google.Outdoors)

20. Tune Set Size (Pangea)

21. Empirical Results

22. Summary Classification accuracy can be improved by factoring in the affinity information implicit in the data to be categorized. How to apply these ideas to other types of classifiers?