1. Divided Pretreatment to Targets and Intentions for Query Recommendation Reporter: Yangyang Kang 2012-11-04 1/23

2. Outline Introduction Related Work Approach Experiment Conclusion and Future Work 2/23

3. Introduction Recommend identical or related queries through understanding user’s query intention Main research form Currently – Relevant or pseudo-relevant text contents – Click or browsing retrieval behaviors Ignore analyze intention from query itself – Spare information – Fuzzy structural relation – Optional component form 3/23

4. Definition Query Target – target entity, behavior or status Query Intention – operations of intention or motivations of retrieval which impose on target Example – Where is Soochow University? – Target: Soochow University – Intention: Where is 4/23

5. Outline Introduction Related Work Approach Experiment Conclusion and Future Work 5/23

6. Related Work Document-based approach – Divide into three categories: global document, local document, manual editing corpus (Nick et al., 2007; Yanan Li et al., 2010) Log-based approach – Divide into two categories: session-based approach, click-based approach(Mei et al., 2008; Cao et al., 2008) 6/23

7. Outline Introduction Related Work Approach Experiment Conclusion and Future Work 7/23

8. Approach Offline-system – Target & Intention Recognition – Intention Cluster Online-system – Query Recommendation 8/23

9. Target & Intention Recognition Query Preprocessing – Beaze-Yates divide query into informational, not informational and ambiguous – We focus on informational queries by following rules Short queries less than two words Titles of news or notice Classifier – NaiveBayes – 10 cross-validations 9/23

10. Target & Intention Recognition Feature Selection – Lexical-based Perspective – Context-based Perspective 10/23

11. Intention Cluster Intention Vector – collection of intention words – To build the associated network of intentions 11/23

12. Intention Cluster Intention Similarity Calculation – Vector Space Model + Cosine Similarity Weight values(TF-IDF) – Language Model + KL Divergence normalize and smooth 12/23

13. Query Recommendation Give a query – Recognize the target and intention of the query – Mine the targets co-occurred with the intention in the global samples – Build the description of intention vector – Measure the similarity with the prior intention clusters – Choose the most similar cluster as candidate intention set according to the similarity ranking – Combine the candidate intention words with the target to form new queries 13/23

14. Outline Introduction Related Work Approach Experiment Conclusion and Future Work 14/23

15. Corpus Sogou2008 query logs – 1,902,402 informational queries and group queries with same clicked URLs Classification experiment – 968 group queries with an average of 5 – Human-label(three volunteers + cross-validation) Cluster experiment – Select 1,981 intention words randomly – Human-label(three volunteers + cross-validation) Recommendation experiment – 2,000 queries randomly – Divide equally to six sample spaces – Six volunteers 15/23

16. Evaluation Method Classification experiment – Precision, recall, f-value and global accuracy – Through statistical, the percentage of target words to intention words is close to 2:1 Cluster experiment – Precision, macro-average Recommendation experiment – Global precision(G-P) – Consistent precision(C-P) – Relevant precision(R-P) – P@n(1≤n≤10) 16/23

17. Results & Analysis Classification experiment – Sys-1: lexical-based approach – Sys-2: Context-based approach – Sys-3: combine two approaches – Baseline1: assume all words in query are targets – Baseline2: assume all words in query are intentions 17/23

18. Results & Analysis Cluster experiment – Sys-VSM : Vector Space Model – Sys-KL : Language Model 18/23

19. Results & Analysis Recommendation experiment 19/23

20. Results & Analysis Recommendation experiment 20/23

21. Outline Introduction Related Work Approach Experiment Conclusion and Future Work 21/23

22. Conclusion & Future work Conclusion – Propose a new QR method which concentrates on query itself – Recognize target and intention classification – Obtain synonymous or related intention words for recommedation Future work – Focus on feature selection method to enhance the intention description – Use semantic intention matching algorithm Divide entity roles Employ into similarity matching process – Analyze the combination of target and intention words, form a fluent and logical query 22/23

23. Thank you! Q & A ！ 23/23