Exploring the Query-Flow Graph with a Mixture Model for Query Recommendation

1. Exploring the Query-Flow Graph with a Mixture Model forQuery Recommendation Lu Bai, JiafengGuo, Xueqi Cheng, XiuboGeng, Pan Du Institute of Computing Technology , CAS

2. Outline • Introduction • Our approach • Experimental results • Conclusion & Future work

3. Introduction • Query recommendation • Generated from web query log • Different types of information are considered, including search results, clickthrough data, search sessions.

4. Introduction • Recently, query-flow graph was introduced into query recommendation. Yahoo  360 Yahoo  Yahoo mail 1 360  Xbox 360  kinect 1 1 1 1 Kinect Xbox 720 1 1 2 Yahoo messenger  Yahoo Yahoo mail  Yahoo messenger 1 1 360  Xbox 360  Xbox 720 apple  Yahoo apple  apple tree

5. Introduction • Traditionally, personalized random walk over query-flow graph was used for recommendation. • Dangling queries • No out links • Nearly 9% of whole queries • Ambiguous queries • Mixed recommendation • Hard to read • Dominant recommendation • Cannot satisfy different needs 1 1 1 1 1 1 1 2 1 1

6. Our Work • Explore query-flow graph for better recommendation • Apply a novel mixture model over query-flow graph to learn the intents of queries. • Perform an intent-biased random walk on the query-flow graph for recommendation.

7. Probabilistic model of generating query-flow graph • Model the generation of the query-flow graph with a novel mixture model • Assumptions • Queries are triggered by query intents. • Consecutive queries in one search session are from the same intent.

8. Probabilistic model of generating query-flow graph • Process of generating a directed edge • Draw an intent indicator from the multinomial distribution . • Draw query nodes from the same multinomial intent distribution , respectively. • Draw the directed edge from a binomial distribution Likelihood function

9. Probabilistic model of generating query-flow graph • EM algorithm is used to estimate parameters • E step • M step

10. Intent-biased random walk • Based on the learned query intents, we apply intent-biased random walk for query recommendation. • Dangling queries: back offto its intents • Ambiguous queries: recommend under the each intent transition probability matrix row normalized weight matrix preference vector , A row vector of query distribution of intent r All entries are zeroes, except that the i-th is 1

11. Experiments • Data Set • A 3-month query log generated from a commercial search engine. • Sessions are split by 30 minutes. • No stemming and no stop words removing. • The biggest connected graph is extracted for experiments, which is consisted of 16,980 queries and 51,214 edges.

12. Experiments • Learning performance on different intent number.

13. Experiments • Learned query intents:

14. Experiments • Dangling query suggestion • Ambiguous query suggestion

15. Experiments • Performance improvement based on user click behaviors

16. Conclusion and Future work • conclusion • We explore the query-flow graph with a novel probabilistic mixture model for learning query intents. • An intent-biased random walk is introduced to integrate the learned intents for recommendation. • Future work • Learn query intents with more auxiliary information: clicks, URLs, words etc.