1. Personalized Web Search by Mapping User Queries to Categories Fang Liu Presented by Jing Zhang CS491CXZ February 26, 2004

2. Background Different users use same key words such as “apple” can be fruit or computer Category hierarchy can’t fit in one screen Users are impatient to identify hierarchy before submit query

3. Central problem How to personalize web search by mapping user queries to categories?

4. Key ideas of this paper Build profile (both user and general profile) on search history Deduce appropriate categories based on user’s profile Associate query key words with category Return top 3 categories to user each time

5. Methods to map key words to category Use both user profile and general profile Use user profile only Use general profile only

6. Build user profiles (1) Tree representation of search record

7. Build user profiles (2) Predefined Input Output

8. Build general profile First two level of ODP category hierarchy (619 categories) Row1 Row2

9. Algorithms to learn profiles Linear Least Squares Fit (LLST) Rocchio-based Algorithm K-Nearest Neighbor (kNN) Adaptive Learning

10. LLSF Singular Value Decomposition

11. Pseudo-LLSF (pLLSF)

12. Ricchio-based Algorithm (bRocchio) where m is the number of documents in DT, N i is the number of documents that are related to the i-th category, and M(i,j) is the average weight of the j-th term in all documents that are related to the i-th category.

13. kNN where q is the query; c j is the j-th category; d i is a document among the k nearest neighbors of q and the i-th row vector in DT, Cos(q, d i ) is the cosine similarity between q and d i, and DC(i,j) denotes whether d i is related to the j-th category.

14. Adaptive Learning (aRocchio)

15. Data sets for the experiment

16. Performance Evaluation where n is the number of related categories to the query, score ci is the score of a related category c i that is ranked among the top 3, rank ci is the rank of c i and ideal_rank ci is the highest possible rank for c i

17. Experiment Results (1) Batch Learning Method

18. Experiment Results (2) Comparison of Mapping methods

19. Experiment Results (3) Adaptive Learning (aRocchio)

20. Discussions Why user 1 have lowest accuracy and user 3 have highest accuracy for batch learning method?