The Recommendation Click Graph: Properties and Applications

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 • 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 • 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 SubgraphFor query qi in a recommendation click graph, we define a local subgraph of qi aswhere

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?