1. 1 Web Query Classification Query Classification Task: map queries to concepts Application: Paid advertisement 问题：百度 /Google 怎么赚钱？

2. 2 Query Classification and Online Advertisement

3. 33 QC as Machine Learning Inspired by the KDDCUP’05 competition Classify a query into a ranked list of categories Queries are collected from real search engines Target categories are organized in a tree with each node being a category

4. 4 How to do it?

5. 55 Solutions: Query Enrichment + Staged Classification Solution 1: Query/Category Enrichment Solution 2: Bridging classifier

6. 66 Category information Full text Query enrichment Textual information Title Snippet Category

7. 77 Classifiers Map by Word Matching Direct and Extended Matching High precision, low recall SVM: Apply synonym- based classifiers to map Web pages from ODP to target taxonomy Obtain as the training data Train SVM classifiers for the target categories; Higher Recall D E

8. 88 Bridging Classifier Problem with Solution 1: When target is changed, training needs to repeat! Solution: Connect the target taxonomy and queries by taking an intermediate taxonomy as a bridge

9. 99 Bridging Classifier (Cont.) How to connect? Prior prob. of The relation between and

10. 10 Category Selection for Intermediate Taxonomy Category Selection for Reducing Complexity Total Probability (TP) Mutual Information

11. 11 11 / 68 Experiment ─ Data Sets & Evaluation KDDCUP Starting at 1997, KDD Cup is the leading Data Mining and Knowledge Discovery competition in the world, organized by ACM SIGKDD KDDCUP 2005 Task: Categorize 800K search queries into 67 categories Three Awards (1) Performance Award ; (2) Precision Award; (3) Creativity Award Participation 142 registration groups; 37 solutions submitted from 32 teams Evaluation data 800 queries randomly selected from the 800K query set 3 human labelers labeled the entire evaluation query set (details)details Evaluation measurements: Precision and Performance (F1) (details)details a

12. 12 12 / 68 Experiment Results ─ Compare Different Methods From Different Groups Comparison among our own methods Comparison with other teams in KDDCUP2005

13. 13 Result of Bridging Classifiers Using bridging classifier allows the target classes to change freely without the need to retrain the classifier! Performance of the Bridging Classifier with Different Granularity of Intermediate Taxonomy

14. 14 Target-transfer Learning Classifier, once trained, stays constant When target classes change, classifier needs to be retrained with new data Too costly Not online Bridging Classifier: Allow target to change Application: advertisements come and go, but our query  target mapping needs not be retrained! We call this the target-transfer learning problem