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Personalized Web Search: Mapping User Queries to Categories by Fang Liu

This paper addresses the challenges of personalizing web search by mapping user queries to relevant categories. It highlights the issue of different user interpretations of similar keywords, such as “apple” representing both a fruit and a computer. By building user and general profiles based on search history, and associating keywords with predefined categories, the study proposes methods to return the top three relevant categories for users. The effectiveness of various algorithms, such as k-Nearest Neighbor and adaptive learning techniques, is evaluated in this innovative approach.

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Personalized Web Search: Mapping User Queries to Categories by Fang Liu

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  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 , Niis 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; cjis the j-th category; diis a document among the k nearest neighbors of q and the i-th row vector in DT , Cos(q, di) is the cosine similarity between q and di , and DC(i,j) denotes whether diis 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, scoreci is the score of a related category ci that is ranked among the top 3, rankciis the rank of ci and ideal_rankci is the highest possible rank for ci

  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?

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