1. The Recommendation Click Graph: Properties and Applications
Yufei Xue, Yiqun Liu, Min Zhang, Shaoping Ma, Liyun Ru
State Key Laboratory of Intelligent Technology and Systems,
Tsinghua National Laboratory for Information Science and Technology,
Department of Computer Science and Technology, Tsinghua University.

2. Introduction Query Recommendation The Clicks on Query Recommendation
Widely used in commercial search engines
Frequently used by users
The Clicks on Query Recommendation
Show users’ search intents
Reflect users are not satisfied with search results

3. Introduction Recommendation Click Graph: Concept Properties
Applications
Optimizing search results
Recognizing ambiguous queries

4. Related Work
Link Analysis on Web Graphs:
HITS
PageRank
TrustRank …

5. Related Work Types of Web Graphs Examples of Web Graphs Word graph
Session graph
URL cover graph
URL link graph
URL terms graph
Examples of Web Graphs
HyperLink Graph
Query Flow Graph

6. Definition
The recommendation click graph is a directed graph Gc =(V, E) , where
the set of nodes, V = {q|q appear in the recommendation click log as a source or destination query}
E={(qi,qj)| < qi,qj >is a recommendation click pair in the recommendation click log}

7. Assumptions For a recommendation click pair < qi,qj >
Assumption 1: qj describes the user's information needs more precisely than qi, or
Assumption 2: qj does not describe the user's information needs more precisely than qi, but the user is interested in qjand wants more information.

8. Properites A Recommendation Click Graph Search log of 31 days
58,334,303 clicks
23,516,620 vertices
31,569,262 directed edges

9. Properites Connected Components 2,668,331 components
71% have only 2 vertices
The largest component has 16,298,916 vertices

10. Local Analysis
Local Subgraph For query qi in a recommendation click graph, we define a local subgraph of qi as where

11. Local Analysis HITS on Local Subgraph
Apply HITS on a local subgraph of the recommendation click graph.
Find the queries with high authority value and low hub value.
These queries may satisfy more users and be less ambiguous.
Use the search results of these queries to optimizing the search result of original query.

12. Optimizing Search Results

13. Optimizing Search Results
Experiment
From the queries with out-degree>8, we randomly selected two groups of queries for experiment.
Group 1: The queries with out-degree = 8 or 9
Group 2: Randomly Selected.

14. Finding Ambiguous Queries
Inverse PageRank
High inverse PageRank Indicates having many outlinks or pointing to vertices with many outlinks.
High inverse PageRank on Recommendation Click Graph indicates strong ambiguity.
Experiment on 1010 queries
Sort the queries by Inverse PageRank in decending order.
Divide the queries in 10 buckets.

15. Finding Ambiguous Queries

16. Finding Ambiguous Queries

17. Finding Ambiguous Queries

18. Conclusions Concept: Recommendation Click Graph
Properties: Similary to Hyperlink Graph
Applications
HITS: Optimizing Search Results
Inverse PageRank: Finding Ambiguous Queries

19. Thanks! Questions?