1. 1 Pengjie Ren, Zhumin Chen and Jun Ma Information Retrieval Lab. Shandong University 报告人：任鹏杰 2013 年 11 月 18 日 Understanding Temporal Intent of User Query based on Time-based Query Classification

2. 2 Outline Why Temporal Intent Detection? Query Temporal Pattern Taxonomy Query Pattern Detection Framework Experiment Results Application Conclusion and Future Work

3. 3 Outline Why Temporal Intent Detection? Query Temporal Pattern Taxonomy Query Pattern Detection Framework Experiment Results Application Conclusion and Future Work

4. 4 Why Temporal Intent Detection?  Richard McCreadie SIGIR 2013 Users tend to prefer rankings that integrate tweets or newswire articles soon after an event breaks, and blogs and Wikipedia pages become more useful over time. Automatic temporal intent detection is very significant for time-sensitive information retrieval, temporal diversity etc.!  Hideo Joho WWW 2013 48.2% seek for information about the same day as they perform the search; 32.7% look for past information; 8.1% look for future information; 10.9% say that their information needs do not have specific temporal attributes.

5. 5 Outline Why Temporal Intent Detection? Query Temporal Pattern Taxonomy Query Pattern Detection Framework Experiment Results Application Conclusion and Future Work

6. 6 In this paper, we propose an approach to identify the different temporal patterns automatically. Different Temporal Patterns Imply Different Temporal Intents Kulkarni A et al. (WSDM 2011) find some temporal patterns of query through mining query logs. However, they do not propose methods to identify those patterns automatically. Query frequency Curves from Google Trend

7. 7 Query Temporal Pattern Taxonomy Java JDK Haiti Earthquake Christmas PresentEarthquake Clearly, we can use spikes to detect query temporal patterns.

8. 8 What is a Spike? A spike is a set of continuous points on the query frequency curve that burst singularly. Generally, it represents an event. Spikes are hard to be detected effectively and precisely. Specially, we found it not effective to learn a cutting line to identify all spikes. Southeast Asia Earthquake Pakistan earthquake China earthquake Haiti earthquake Japan earthquake Virginia earthquake

9. 9 Outline Why Temporal Intent Detection? Query Temporal Pattern Taxonomy Query Pattern Detection Framework Experiment Results Application Conclusion and Future Work

10. 10 Query Classification System Query Pattern Detection Framework Training Set Query Log Feature Extraction Query frequency curves Query Classifier (SVM) Query Pattern Preprocess

11. 11 (1). Preprocess Trend Component Seasonal Component Random Component Use polynomial regression to model Trend Component. According to time series analysis, any curve contains three components. This is what we care in this paper. So we should remove Trend Component.

12. 12 We use Student-t Distribution instead of Gaussian Distribution because we do not have exact training data pair (X, m t ). We have to use (X,F) instead. Thus, St and Yt components become noise when training. Student-t Distribution is more robust to noise than Gaussian Distribution. From PRML Student-t Gaussian noise without noise both work well Log likelihood loss function (1). Preprocess

13. 13 Original Query Curve Trend Component Seasonal & Random Component (1). Preprocess

14. 14 (2). Feature Extraction Mean Standard Deviation MR (Max Rate) SR (Spike Rate) Basic Features Curve Distance Features Regression Features For preprocessed query frequency curves, we define following features. D QoT D OQ D AMQ D PMQ Cutoff Spikes PD(Periodic Deviation)

15. 15 MR (Max Rate)

16. 16 SR (Spike Rate) MQ OQQoT m is half the period of a spike.

17. 17 How to determine the value of m? SR (Spike Rate)

18. 18 Distance between Two Curves Fi q :shifting time series Fi by q time units. || ||:the l2 norm. This measure finds the optimal alignment (translation q) and the scaling coefficient α for matching the shapes of the two time series. It is difficult to find the optimum solution. In practice, we shift all possible q to find the approximation solution. Jaewon Yang and Jure Leskovec. Patterns of temporal variation in online media. WSDM, 2011.

19. 19 Jaewon Yang and Jure Leskovec. Patterns of temporal variation in online media. WSDM, 2011. Distance between Two Curves

20. 20 D QoT D OQ D AMQ D PMQ D QoT : Average distance from annotated QoT curves. D OQ : Average distance from annotated OQ curves. D AMQ : Average distance from annotated AMQ curves. D PMQ : Average distance from annotated PMQ curves. Similar to KNN but cost much less time.

21. 21 Cutoff Spikes PD What about training data? (F, Cutoff) pair is not known. PD: Measure periodicity …… Spikes: Number of spikes …… Above 8 features are combined to learn a cutting off line We can use annotated pair (F, Pattern Category) to approximate (F, Cutoff). For this curve, because we annotate it as MQ, the cutoff value line in the pink area.

22. 22 Outline Why Temporal Intent Detection? Query Temporal Pattern Taxonomy Query Pattern Detection Framework Experiment Results Application Conclusion and Future Work

23. Experiment Results  5,000 queries from Query Track 07-09 of TREC.  Corresponding query frequency files from Google Trends.  Manually annotate categories of these queries in terms of their frequency curves.  5-fold Query ClassQoTOQAMQPMQaverage P0.9520.9280.8460.9140.910 R0.9730.9150.8310.9240.911 F1F10.9620.9220.8380.9190.910 Classification Performance Comparison for Different Query Categories AMQ PMQ QoT OQ

24. 24 Feature Effectiveness Analysis

25. 25 Outline Why Temporal Intent Detection? Query Temporal Pattern Taxonomy Query Pattern Detection Framework Experiment Results Application Conclusion and Future Work

26. 26 Application – Temporal Diversity Temporal intents of user query are uncertain, we should diversify the search results in time dimension in order to cover more important time unit of user query. Temporal Intent Coverage Subtopic Coverage Novelty

27. 27 Application – Temporal Diversity MMRSIGIR’98 xQuADWWW’10 IA-SelectWSDM’09 LM+T+DSIGIR’13 RM+T+S+DOur method

28. 28 Outline Why Temporal Intent Detection? Query Temporal Pattern Taxonomy Query Pattern Detection Framework Experiment Results Application Conclusion and Future Work

29. Conclusion  We shift the problem of temporal intents detection to classification problem.  We propose effective features to detect temporal intents effectively.  We imply temporal intents results to temporal diversity and achieve high performance. 29

30. 30 Future Work More Effective Features Data sparse problem for long queries

31. 31 Thanks a lot for your attention!