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Learning to Rank for Information Retrieval

Learning to Rank for Information Retrieval. Liang Du Supervised by Prof. Yi-Dong Shen. Outline. Motivation Learning to Rank Related work. Motivation. We are drawn in information, but we are starveling for knowledge. Information is nothing without retrieval

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Learning to Rank for Information Retrieval

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  1. Learning to Rank for Information Retrieval Liang Du Supervised by Prof. Yi-Dong Shen

  2. Outline • Motivation • Learning to Rank • Related work

  3. Motivation • We are drawn in information, but we are starveling for knowledge. Information is nothing without retrieval • Search Engine are widely used tools • The key inside search engine is ranking model

  4. Inside Search Engine

  5. IR Evaluation • Various measures are used • MAP • NDCG • WTA • MRR

  6. Ranking Model • Conventional Ranking Models • Similarity-based models (like vector space model) • Probabilistic models (like Language model ) • Hyperlink-based models (like PageRank)

  7. Discussions on Conventional Ranking Models • For a particular model –Parameter tuning is usually difficult, especially when there are many parameters to tune. •For comparison between two models –Given a test set, it is difficult to compare two models, one is over-tuned (over-fitting) while the other is not. •For a collection of models –There are hundreds of models proposed in the literature. –It is non-trivial to combine them effectively.

  8. Learning to Rank • Machine learning is an effective tool for ranking • To automatically tune parameters • To combine multiple evidences • To avoid over-fitting (regularization framework, structure risk minimization, etc.)

  9. Framework of Learning to Rank for IR

  10. Categorization-1 • Criteria: Relation to Conventional Machine Learning • Learning to rank reduced to conventional machine learning. • Ranking with IR unique properties

  11. Categorization-1 • Criteria: basic unit of learning • Point wise (Input: single documents ) • Pairwise (Input: document pairs ) • Listwise (Input: document collections )

  12. Hot Area • More than 40 papers directly for learning to rank and 100+ published on top conference and journals in recent five years.

  13. Related Areas • Machine learning (provide theory analysis and practice algorithms for ranking) • Data Mining (supply evidence and algorithms for ranking) • Information Retrieval (provide test bed and application background)

  14. Any Questions?

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