Post on 15-Jul-2015
+
Extending Faceted Search to the General Web
2014/11/25 (Tue.)�Chang Wei-Yuan @ MakeLab Group Meeting
Weize Kong, James Allan �CIKM‘14
+Outline
n Introduction �
n Method �n Facet Generation �n Facet Feedback �n Evaluation �
n Experiment �
n Conclusion �
n Thought
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+Outline
n Introduction �
n Method �n Facet Generation �n Facet Feedback �n Evaluation �
n Experiment �
n Conclusion �
n Thought
3
+ Introduction
n Faceted search helps users by offering drill-down options as a complement to the keyword input box.
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+ Introduction
n However, this idea is not well explored for general web search. �n heterogeneous nature �
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+ Introduction
n However, this idea is not well explored for general web search. �n heterogeneous nature �
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baggage allowance
所有航線
所有航線
國內航線
國際航線
貨運公司 行李類型
+ Introduction
n However, this idea is not well explored for general web search. �n heterogeneous nature �
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baggage allowance
所有航線
所有航線
國內航線
國際航線
貨運公司 行李類型
← query
← facet
← facet term
↓ search result ( ducument)
+ Introduction
n Goal : �n query-dependent automatic facet generation �n user feedback on these query facets into
document ranking
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+Outline
n Introduction �
n Method �n Facet Generation �n Facet Feedback �n Evaluation �
n Experiment �
n Conclusion �
n Thought
9
+Flow Chart 10
Search Result
Candidate Facets Facets
Selected Terms
Top-ranked Documents
Search Result
Query Extracting
Candidates Refining
Candidates
Facet Feedback
+Flow Chart 11
Search Result
Candidate Facets Facets
Selected Terms
Top-ranked Documents
Search Result
Query Extracting
Candidates Refining
Candidates
Facet Feedback
+Facet
Generation Facet
Feedback Evaluation
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n Input : Query and Search Result�
n Step 1 : Extracting Candidates �
n Step 2 : Refining Candidates �
n Output : Query Facet �
+Facet
Generation Facet
Feedback Evaluation
13
n Step 1 : Extracting Candidates �n applied both textual and HTML patterns on
the top search results �
+Facet
Generation Facet
Feedback Evaluation
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n Step 1 : Extracting Candidates �
n query : “mars landing”�
n search results �n “ Mars rovers such as Curiosity, Opportunity
and Spirit ”�
n candidate facets �n C : { Curiosity, Opportunity, Spirit } �
+Facet
Generation Facet
Feedback Evaluation
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n Step 1 : Extracting Candidates �
n the candidate query facets extracted. �n noisy�n non-relevant to the issued query�n terms be not members of the same class �
+Facet
Generation Facet
Feedback Evaluation
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n Step 1 : Extracting Candidates �
n query : “mars landing”�
n candidate facets : �
+Facet
Generation Facet
Feedback Evaluation
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n Step 1 : Extracting Candidates �
n query : “mars landing”�
n candidate facets : �
+Facet
Generation Facet
Feedback Evaluation
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n Step 1 : Extracting Candidates �
n query : “mars landing”�
n candidate facets : �
n Refine �
+Facet
Generation Facet
Feedback Evaluation
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n Step 2 : Refining Candidates �n re-cluster the query facets or their facet
terms into higher quality query facets �
+Facet
Generation Facet
Feedback Evaluation
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n Step 2 : Refining Candidates �
n Topic modeling �n pLSA, LDA�
n Unsupervised clustering method �n QDMiner, QDM �
n Super-vised methods based on a graphical model �n QF-I, QF-J �
+Facet
Generation Facet
Feedback Evaluation
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n Input : Query and Search Result�
n Step 1 : Extracting Candidates �
n Step 2 : Refining Candidates �
n Output : Facet : { a set of terms } �n Year : { 2007, 2011, 2012 } �n Lab : { NASA, Mars Science Lab, Curiosity Lab } �
�
+Flow Chart 22
Search Result
Candidate Facets Facets
Selected Terms
Top-ranked Documents
Search Result
Query Extracting
Candidates Refining
Candidates
Facet Feedback
+Facet
Generation Facet
Feedback Evaluation
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n Input : Document, Query, User Selection �n Document = one of search result �
n Boolean Filtering Model �
n Soft Ranking Model �
n Output : the score of each document
+Facet
Generation Facet
Feedback Evaluation
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n Boolean Filtering Model �
n Fu denotes the set of feedback facets which user selected �
n condition B can be either AND, OR, or A+O �n S(D, Q) is the score returned by the original
retrieval model �
+Facet
Generation Facet
Feedback Evaluation
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n Soft Ranking Model �
n λ is a parameter for adjusting the weight �n SE(D, Fu) is the expansion part which captures
the relevance between the document and feedback facet�
+Facet
Generation Facet
Feedback Evaluation
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n Input : Documents, Query, User Selection �
n Boolean Filtering Model �
n Soft Ranking Model �
n Output : the score of each document
+Facet
Generation Facet
Feedback Evaluation
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n Intrinsic Evaluation �n Ground Truth: query facets are constructed
by human annotators �n annotators are asked to group or re-group
terms in the pool into preferred query facets. �n pooling facets generated by the different systems �
n compared with facets generated by different systems �
+Facet
Generation Facet
Feedback Evaluation
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n Extrinsic Evaluation �n User Model �
n The user model describes how a user selects feedback terms from facets, based on which we can estimate the time cost for the user.
↑ time for scanning facet
time for selecting terms
↓
+Facet
Generation Facet
Feedback Evaluation
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n Extrinsic Evaluation �n Oracle Feedback and Annotator Feedback �
n Oracle feedback model only selected effective terms as feedback. �
n The annotator is asked to select all the terms from the facets that would help address the information need. �
+Outline
n Introduction �
n Method �n Facet Generation �n Facet Feedback �n Evaluation �
n Experiment �
n Conclusion �
n Thought
30
+Experiment Settings
n Dataset �n For the document corpus, we use the ClueWeb09
Category-B collection. �n 196 queries and 678 query subtopics �
n Facet Generation Models �n pLSA, LDA, QDM, QF-I and QF-J �
n Facet Feedback Models �n Boolean filtering models, soft ranking models �
n Baseline Retrieval Model �n SDM, and its MAP(Mean average precision) = 0.185 �
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+Facet Generation Models 33
based on annotator feedback and SF feedback model
based on oracle feedback and SF feedback model.
+Facet Generation Models 34
based on annotator feedback and SF feedback model
based on oracle feedback and SF feedback model.
Our experiments testify to the potential of Faceted Web Search.
+Outline
n Introduction �
n Method �n Facet Generation �n Facet Feedback �n Evaluation �
n Experiment �
n Conclusion �
n Thought
37
+Conclusion
n This paper proposed Faceted Web Search. �n an extension of faceted search to the general
Web �
n query-dependent automatic facet generation �
n feedback on these query facets into document ranking
38
+Outline
n Introduction �
n Method �n Facet Generation �n Facet Feedback �n Evaluation �
n Experiment �
n Conclusion �
n Thought
39