The document discusses search engine ranking and optimization. It introduces problems with existing ranking models such as not considering factors like trust bias. A new methodology is proposed that makes more realistic assumptions about user behavior, such as modeling user sessions and preferences for certain websites. The approach aims to improve search result ranking by incorporating additional ranking factors from click logs and evaluating the model using standard information retrieval metrics on search log and benchmark datasets.

1. Dr. Carson Kai-Sang Leung
Inderjeet Singh
(Database and Data Mining Lab)

2.  Introduction
 Problem
 Solution Methodology
 Evaluation
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4.  Mining user behaviour/preferences
 Predict document relevance
 Re-rank the search results
 Compare different ranking functions (train/test)
 Optimize the ad. performance
 Query suggestions
 How Big are these logs?
◦ 10+ terabyte of entries each day
◦ Composed of billions of distinct (query, url)’s
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5. Documents/results Ranking factors
Many ranking factors
presented in order of depend on query,
considered when
the relevance to the document and
ranking these results
query query-document pair
Improving ranking
based on user Personalized search Recency (temporal)
preferences +Social search ranking
(likes/dislikes)
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6. [David Green; blog]
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7. # of clicks received
[CIKM'09 Tutorial]
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8. Trust factor: Preferences to certain URLs more than the other,
e.g., wikipedia.com, stackoverflow.com, Yahoo answers,
about.com
What is missing (in previous models) ?
 Modelling trust factor
 Clicks on sponsored results
 Related queries/searches (sidebars)
 Realistic and flexible assumptions on user behaviour
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10. 1. Informational query – “DDR3 memory”, “SATA 3 hard drives”,
“American history”
2. Navigational query – “gmail”, “digg”, “CIBC”, “CIBC credit cards”
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11. No No
Snippet Examine? Snippet Examine?
No
Yes Yes
No
Snippet Attractive? Snippet Attractive?
No
Yes No Yes
Enough Utility? Enough Utility?
Yes Yes
End End
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12. Realistic and flexible assumptions on user
behaviour (session modelling)
Consider trust bias (trust factor)
Order results for particular query by
relevance scores predicted by model
Comparison of this order to the editorial
ranking
Is it good model? If orderings agree upto a
considerable extent
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13. Deploy this model as a feature/factor for predicting relevance in learning
to rank algorithm
Deriving retrieval/ranking function
If metric gains over baseline ranking function? Model insights can be used
as a feature in ranking function
Ranking function tests with different class of queries for metric gains
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14. Metrics
• Discounted Cumulative Gain (DCG)
• Normalized DCG (NDCG)
• Precision
• Recall
Two types of data
1. Search click logs (from real or meta search engines)
2. Benchmarking dataset LEarning TO Rank (LETOR) for
information retrieval
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15. [Guo et al., 2009]
[Chapelle and Zhang, 2009]
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16.  David Green Blog. http://davidgreen.com/comparative-value-of-google-search-
rankings (accessed 20th-April-2011)
 Fan Guo and Chao Liu. Statistical Models for Web Search Click Log Analysis.
Tutorial, 2009
 Fan Guo, Chao Liu, and Yi Min Wang. Efficient multiple-click models in web
search. In Proceedings of Second Web Search and Data Mining (WSDM)
Conference, Barcelona, Spain, pages 124-131. ACM, 9-11 February, 2009
 Olivier Chapelle and Ye Zhang. A dynamic bayesian network click model for
web search and ranking. In Proceedings of the 18th International Conference
on World Wide web (WWW), Madrid, Spain, pages 1-10, ACM, 20-24 April,
2009
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17. [Tmcnet.com Blog]
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