Beyond Basic Faceted SearchBen-Yitzhak, et al.

Fahimeh FakourCS 572Summer 2010

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

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)