Navigation-Aided%20Retrieval

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Navigation-Aided Retrieval

• Shashank Pandit and Christopher Olstony

Presentation by Yang Yu CSE 450 Web Data Mining
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Outline

• Introduction
• Related Work
• System Model
• Prototype System
• Evaluation
• Summary Future Work

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Introduction

• Background reasons for this work
• Difficulty in formulating appropriate queries
• Open-ended search tasks
• Preference for orienteering
• Navigation-Aided
• Retrieval

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Introduction

• Organic versus Synthetic Structure
• One is trying to synthesize structure
  automatically into query results
• One is trying to use structure that naturally
  exists in documents
• Advantages of organic NAR
• Human oversight.
• Familiar user interface.
• A single view of the document collection.
• Robust implementation by a third party
• Contributions
• Formal model of navigation-aided retrieval
• An overview of techniques for a NAR-based
  retrieval system
• Empirical evaluation via a user study

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Related Work

• Selecting Starting Points
• Best Trails system
• An ad-hoc scoring function for starting points
• Restrict starting points to be documents that
  themselves match the query
• It does not take into account navigability
  factors
• User interface departs substantially from the
  traditional interface
• Topic distillation that mainly uses HITS
• Only effective for broad topic areas for which
  there are many hubs and authorities
• Guiding Navigation
• WebWatcher highlights hyperlinks along paths
  taken by previous users who had posed similar
  queries.

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System Model

• Generic Model
• Query submodel
• Navigation submodel
• generic scoring function
• Assumption every member of relevance set St is a
  singleton set.
• Fatten" St into d1, d2, , dn.

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System Model

• Instantiations of Generic Model
• Conventional Probabilistic IR Model
• Navigation-Conscious Model
• The two terms embody the two key factors
• the number of documents reachable from d that are
  relevant to the search
• task
• the ease and accuracy with which the user is able
  to navigate to those documents.

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Prototype System

• Preprocessing
• Content Engine
• Connectivity Engine ltd1, d2, dW, W(N(d2), d1,
  d2)gt
• Intermediary

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Prototype System
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Prototype System

• Selecting Starting Points
• 1. Retrieve from the content engine all documents
  d relevant to q.
• 2. For each relevant document d retrieved in
  Step 1, then retrieve from the connectivity
  engine all documents d that can navigate to d
• 3. For each unique document d in Step 2, compute
  the starting point score
• 4. Sort the documents in decreasing order of this
  score, truncate after the top k documents.

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Prototype System

• Adding Navigation Guidance
• 1. Retrieve from the content engine all documents
  d for which R(d, q)gt T
• 2. For each document d retrieved in Step 1,
  retrieve from the connectivity engine the tuple
  corresponding to ltd, dgt, if it exists.
• 3. For each ltd1, d2, dW, W(N(d2), d1, d2)gt tuple
  retrieved in Step 2, highlight links on d that
  point to dW.
• Efficiency and Scalability

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Evaluation

• Experimental Hypotheses
• In query-only scenarios, Volant does not perform
  significantly worse
• In combined query/navigation scenarios, Volant
  performs better
• The best organic starting point is of higher
  quality than one that can be synthesized using
  existing techniques.
• Search Task Test Sets
• Unambiguous
• Ambiguous
• Performance on Unambiguous Queries

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Evaluation

• Performance on Ambiguous Queries
• 4 Criteria - Breadth Accessibility Appeal
  Usefulness.

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Summary and Future Work

• Summary
• Effectiveness
• Relationship to conventional IR
• Relationship to synthetic approaches
• Future Work
• Add redundancy to corpora
• Tune scoring function to be applicable for
  synthetic starting points
• Unified method can both for exploration and
  directly return document

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• Thank you!
• Questions or Comments?