Beyond Basic Faceted Search Ben-Yitzhak, et al.
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Beyond Basic Faceted SearchBen-Yitzhak, et al.
Fahimeh FakourCS 572Summer 2010
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Introduction1. Importance and significance2. Background Information3. Objective4. Related work5. Approach and Solutions6. Enhancements7. Contributions8. Pros & Cons
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1. Importance and Significance
• Too much info• Transactions
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1. Importance and Significance (cont)
• Categories, lists, and the human mind
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• Research done in IBM & Yahoo Research labs
• Facets, buckets, and categories– Navigate multiple paths for different
ordering
• Free text queries• List of matching
documents with count
2. Background Information
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3. Objective
• Extend traditional facet – Beyond numbersNumbers
Words
• Search & Index correlated documents
• Similarity to OLAP: multi-dimensional data
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4. Related Work• Multifaceted search– Lexical subsumption– Synsets and hypernym– RawSugar social tagging
• Online Analytical Processing (OLAP)– Multi-dimensional data– Aggregation of data: Cube
• N-dimensional “group by”
Exciting new technique
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5. Approach & Solutions5.1 Technologies: Lucene & Solr5.2 Data Model5.3 Facet hierarchy: Forest5.4 Creating the facet paths5.5 Running the facet query5.6 Example
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5.1. Technologies: Lucene & Solr
• Posting element:
docID, offset, payload
• Matching document processing
byte array of additional info (runtime accessible)
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5.2. Data Model• Taxonomy: hierarchical
relationships among facets– Predefined taxonomy– Acquired/Learned through
documents
• Facet-path forest– Tree: top-level facet
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5.3. Facet hierarchy: Forest
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5.4. Creating the facet paths
• Posting element for document for each prefix of Pi
• Add path to taxonomy index• Encode all k paths related to this
document
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5.5. Running the facet query
• Terms:– Faceted query string + taxonomy
subtrees– Faceted result set ranked list of
documents matching query + counters
• Lucene: use the Taxonomy Index function to determine ordinal number of paths
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5.6. Example
Facet$clothing: doc1,doc2Facet$clothing$children’s: doc1Facet$clothing: doc1,doc2Facet$clothing$children’s: doc1
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6. Enhancements
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6.1. Business Intelligence• Qualitative rather than quantitative– Best sellers rather than number of
books published by author
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6.2. Dynamic Facets: Welcome to the real world
• Not always independent data
• Example:– Running shorts • Different sizes per color• Location & price
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6.2. Dynamic Facets: Solution
• Use tree over the data
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6.2. Dynamic Facets: Solution (cont)
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7. Contributions• “rich” aggregation : qualitative• Engineering details• Correlation in facet values
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8.1. Pros • Detailed description of engineering
aspects & design decisions• Use of implemented technologies• Clearly defines the scope of the paper• Give foundation/background
information• Compatible with real life data
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8.2. Cons • Experiments and testing: No
qualitative measurement– effectiveness of “qualitative” facets
• Not explain relevance of some of the previous work
• Criteria for display/grouping?– Key use cases & known user access
patterns not explained
• Build taxonomy: depth/breadth?
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Thank You
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References
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Ben-Yitzhak, et al. “Beyond Basic Faceted Search”. Proceedings of the international conference on Web search and web data mining. Pp.33-44, 2008. <http://nadav.harel.org.il/papers/p33-ben-yitzhak.pdf>
“Faceted Search with Solr” Lucid Imagination. July 1, 2010. <http://www.lucidimagination.com/Community/Hear-from-the-Experts/Articles/Faceted-Search-Solr >
“Faceted classification” Wikipedia. July 7, 2010 <http://en.wikipedia.org/wiki/Faceted_classification >
Lemieux, Earley, and Associates. “Designing for Faceted Search” User Interface Engineering. July 6, 2010 <http://www.uie.com/articles/faceted_search/>(Originally in KM World, March 2009)
Mattman, Chris. “Query Models” (presentation slides for class)