1. Mining Query Subtopics from Search Log Data
Hu et al., SIGIR’12
Presented by Baichuan Li

2. Outline Problem Two observations Approach Results Applications
Conclusion

3. Problem
Intention != Query
Many queries are ambiguous or multifaceted

4. Problem

5. Problem Xbox Manchester Homepage? Online game? Where to buy?
Manchester news?
Manchester tourism?
Manchester weather?
Manchester united?

6. Solution
Identifying the major subtopics of a query from the search log data
Personalized search
Query suggestion
Search result presentation
Clustering
Re-ranking
Diversification

7. Observations from The Logs
One subtopic per search (OSS)
If a user clicks multiple URLs after submitting a query, then the clicked URLs tend to represent the same subtopic

9. Observations from The Logs
Subtopic clarification by additional keyword (SCAK)
Users often add additional keywords (in most cases, one additional keyword) to a query to expand the query in order to clarify its subtopic
The URLs clicked after searching both with the original and the expanded queries tend to represent the same subtopic
The key word tends to be indicative of the subtopic
E.g.
harry shum microsoft

10. One Subtopic per Search
Use it as the rule for subtopic identification
10,000 groups of multiclicks of individual queries
The rule is accurate when all the URLs within the multi-clicks are about the same sense or face

11. Accuracy V.S. Frequency

12. Accuracy v.s. Click Position

13. Distribution
The queries with higher frequencies in search log data are more likely to have multi-clicks

14. Subtopic Clarification by Additional Keyword
The keywords ‘microsoft’ and ‘jr’ can be used to represent the two groups (subtopics) respectively

15. Query Types ‘Q’: the query is a single phrase ‘Q + W’: ‘Q’ + a keyword
‘W + Q’: a keyword + ‘Q’
‘Others’

16. Subtopic Overlap and URL Overlap
Randomly selected 500 pairs of queries with the forms ‘Q’ and ‘Q + W’
If subtopics of an expanded query are contained in subtopics of the original query, then there is ‘subtopic overlap’
Check whether two queries share identical clicked URLs (‘URL overlap’)

17. Distribution
The more popular (frequent) a query is, the more likely the rule is applicable

18. Mining Subtopics

19. Preprocessing

20. Clustering Similarity function
The similarity function between URLs ui and uj :
S1: based on the OSS phenomenon
where and denote the vectors of multi-clicks of ui and uj respectively
S2: based on the SCAK phenomenon
S3: based on string similarities

21. SCAK Similarity (S2)
where and denote the vectors of keywords associated with ui and uj respectively

22. Data TREC Data: TREC search result diversification track 3 in 2009
DataSetA and DataSetB: queries and URLs randomly sampled from the logs of the commercial search engine

23. Results

24. Application: Search Result Reranking
The user is first asked which subtopic she is interested in, with the subtopics shown at the top of the results page
When the user selects a subtopic, the URLs belonging to the subtopic will be moved to the top (re-ranked)
The relative order between URLs inside and outside of the subtopic will be kept

25. Example

26. Evaluation Data Results
Collect search log data of 20, 000 randomly selected searches
Each query has at least two subtopics mined by the method
Results
The average position of last clicked URLs is 3.41
Assume the cost for the user to check the subtopics and click one of them is 1.0
The average position of last clicked URLs belonging to the same subtopics is 1.80

27. Conclusion Study the problem of query subtopic mining
Discovered two phenomena of user search behavior
one subtopic per search
Subtopic clarification by additional keyword
Design a novel similarity function
Applications on search result reranking (and search result clustering)
Problem
Can only be employed when there is enough search log data