Personalized Query Expansion for the Web Paul-Alexandru Chirita, Claudiu S. Firan, Wolfgang Nejdl Gabriel Barata.

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Presentation transcript:

Personalized Query Expansion for the Web Paul-Alexandru Chirita, Claudiu S. Firan, Wolfgang Nejdl Gabriel Barata

Motivation

What is query expansion? Add meaningful search terms to the query…

What is PIR based query expansion? Add meaningful search terms to the query… … related to the use’s interests.

Why PIR based query expansion? More personalization quality! More privacy!

Example Google search: “canon book”

Example Top 3 results: The Canon: A Whirligig Tour of the Beautiful Basics of Science Amazon Western Wikipedia Biblical Wikipedia

Example Top 3 results: The Canon: A Whirligig Tour of the Beautiful Basics of Science Amazon Western Wikipedia Biblical Wikipedia

Example Expanded query: “canon book bible”

Example Top 3 results: Biblical Wikipedia Books of the Wikipedia The Canon of the catholicapologetics.org

Query Expansion using Desktop data

Algorithms Expanding with Local Desktop Analysis Expanding with Global Desktop Analysis

Algorithms Expanding with Local Desktop Analysis Expanding with Global Desktop Analysis

Expanding with Local Desktop Analysis Term and Document Frequency Lexical Compounds Sentence Selection

Expanding with Local Desktop Analysis Term and Document Frequency Lexical Compounds Sentence Selection

Term and Document Frequency

Expanding with Local Desktop Analysis Term and Document Frequency Lexical Compounds Sentence Selection

Lexical Compounds { adjective? Noun+ }

Expanding with Local Desktop Analysis Term and Document Frequency Lexical Compounds Sentence Selection

Expanding with Global Desktop Analysis Term Co-occurrence Statistics Thesaurus based Expansion

Expanding with Global Desktop Analysis Term Co-occurrence Statistics Thesaurus based Expansion

Term Co-occurrence Statistics

Expanding with Global Desktop Analysis Term Co-occurrence Statistics Thesaurus based Expansion

Experiments & Evaluation

Experiments 18 users Files indexed within user selected paths, s and Web cache

Experiments They chose 4 queries: – 1 from the top 2% log queries (avg. length = 2.0) – 1 random log query (avg. length = 2.3) – 1 self-selected specific query (avg. length = 2.9) – 1 self-selected ambiguous query (avg. length = 1.8)

Evaluation

Evaluated algorithms: – Google: Google query output – TF, DF: Term and Document Frequency – LC, LC[O]: Regular and Optimized Lexical Compounds – TC[CS], TC[MI], TC[LR]: Term Co-occurrences Statistics using Cosine Similarity, Mutual Information and Likelihood Ratio – WN[SYN], WN[SUB], WN[SUP]: WordNet based expansion with synonyms, sub-concepts and super- concepts.

Results Log queries:

Results Self-selected queries:

Introducing Adaptativity

Query Clarity

Adaptive Expansion

Experiments Same experimental setup as for the previous analyzis.

Results Log queries:

Results Self-selected queries:

Results

Conclusions

Five techniques for determining expansion terms from personal documents. Empirical analysis showed that these approaches perform very well. Expansion process adapts accordingly to query features. Adaptive expansion process proved to yield significant improvements over the static one.

End Any questions?