Boosting the Ranking Function Learning Process using Clustering

1. Boosting the Ranking Function Learning Process using Clustering WIDM 2008

2. Outline • Introduction • Problem definition • Approach • Evaluation • Conclusion

3. Introduction • Abstract • Web continuously grows, the results returned by search engines are too many to review • User feedback has gained a lot of attention • Require a big amount of user feedback on the results • Goal: • Produce user feedback “automatically” by using some methods

4. Problem definition • User feedback • Explicit feedback (user relevnacejudgement) • Implicit feedback • Click information • Users usually inspect only the first few results returned by a search engine, and click even fewer • Collect relevance judgements from clickthrough data is time consuming process • Problem • How to use explicit feedback to generate implicit feedback?(relevance relations expansion)

5. Approach procedure • Process • Assume that only the relevance judgements of the top-10 results are available for each query (by BM25 feature) • Group all the search results into clusters of documents having similar content • Expand the initial set(top-10 results) of relevance judgements using cluster information

6. Clustering • Represent each document by a feature vector • total number of distinct terms in all documents • Cluster method • Bisetion clustering • Similarity • Cosine similarity

7. Relation expansion Train query Train query expansion

8. Relation expansion • Expansion Algorithm:

9. Evaluation • Dataset • Letor OHSUMED collection • 348,566 records and 16,140 relevance judgements • 84 training queries and 22 testing queries • Relevance judgement • 0(irrelevant), 1(partially relevant), 2(strongly relevant) • Training method • RankSVM

10. Evaluation • Clustering precision

11. Evaluation Use 160 relevance judgements

12. Conclusion • We presented a methodology for increasing the training input of ranking function learning systems • Future work • Decision on whether a cluster is valid • Different Cluster label ways