1. Navigation-Aided Retrieval
Shashank Pandit and Christopher Olstony
Presentation by Yang Yu
CSE 450 Web Data Mining

2. Outline Introduction Related Work System Model Prototype System
Evaluation
Summary & Future Work

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

4. Introduction Organic versus Synthetic Structure Contributions
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

5. Related Work Selecting Starting Points Guiding Navigation
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.

6. 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}.

7. 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.

8. Prototype System Preprocessing Content Engine
Connectivity Engine: <d1, d2, dW, W(N(d2), d1, d2)>
Intermediary

9. Prototype System

10. 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.

11. Prototype System Adding Navigation Guidance Efficiency and Scalability
1. Retrieve from the content engine all documents d’ for which R(d’, q)>= T;
2. For each document d’ retrieved in Step 1, retrieve from the connectivity engine the tuple corresponding to <d, d’>, if it exists.
3. For each <d1, d2, dW, W(N(d2), d1, d2)> tuple retrieved in Step 2, highlight links on d that point to dW.
Efficiency and Scalability

12. Evaluation Experimental Hypotheses Search Task Test Sets
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

13. Evaluation Performance on Ambiguous Queries
4 Criteria - Breadth; Accessibility; Appeal; Usefulness.

14. Summary and Future Work
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

15. Thank you!
Questions or Comments?