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Pengjie Ren, Zhumin Chen and Jun MaInformation 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

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Outline

Why Temporal Intent Detection?Query Temporal Pattern TaxonomyQuery Pattern Detection FrameworkExperiment ResultsApplicationConclusion and Future Work

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Outline

Why Temporal Intent Detection?Query Temporal Pattern TaxonomyQuery Pattern Detection FrameworkExperiment ResultsApplicationConclusion and Future Work

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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 201348.2% 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.

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Outline

Why Temporal Intent Detection?Query Temporal Pattern TaxonomyQuery Pattern Detection FrameworkExperiment ResultsApplicationConclusion and Future Work

6In 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

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Query Temporal Pattern Taxonomy

Java JDK

Haiti Earthquake

Christmas PresentEarthquake

Clearly, we can use spikes to detect query temporal patterns.

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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

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Outline

Why Temporal Intent Detection?Query Temporal Pattern TaxonomyQuery Pattern Detection FrameworkExperiment ResultsApplicationConclusion and Future Work

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Query Classification System

Query Pattern Detection Framework

Training Set

Query Log

Feature Extraction

Query frequency curves

Query

Classifier(SVM)

QueryPattern

Preprocess

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(1). Preprocess

ttt YsmF

Trend ComponentSeasonal 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.

;T ξmt xw

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);,,0|(~ ξ StξWe use Student-t Distribution instead of Gaussian Distribution because we do not have exact training data pair (X, mt). 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

||;||2

1),,|(log),,(

1

T wxww

n

iiifStL Log likelihood loss function

(1). Preprocess

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Original Query Curve

Trend Component

Seasonal & Random Component

(1). Preprocess

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(2). Feature Extraction

MeanStandard DeviationMR (Max Rate)SR (Spike Rate)

Basic Features

Curve Distance Features

Regression Features

For preprocessed query frequency curves, we define following features.

DQoT

DOQ

DAMQ

DPMQ

CutoffSpikesPD(Periodic Deviation)

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MR (Max Rate)

tt

M

f

fMR

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SR (Spike Rate)

tt

mMMMMmMNM

f

fffffffffSR

}),...,,,,...,{},...,,max({ 1121

}),...,,max({ 21 NM ffff

MQ

OQ QoT

m is half the period of a spike.

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How to determine the value of m?

MjMMMMiM frfffff },...,,,,...,{ 11

SR (Spike Rate)

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Distance between Two Curves

Fiq :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.

)1)(||1||

||21||(min)2,1(tan

, F

FFFFceDis q

q

Jaewon Yang and Jure Leskovec. Patterns of temporal variation in online media. WSDM, 2011.

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

Distance between Two Curves

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DQoT DOQ DAMQ DPMQ

DQoT: Average distance from annotated QoT curves.DOQ : Average distance from annotated OQ curves.DAMQ : Average distance from annotated AMQ curves.DPMQ : Average distance from annotated PMQ curves.

Similar to KNN but cost much less time.

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Cutoff Spikes PD

What about training data? (F, Cutoff) pair is not known.

XWTCutoff

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.

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Outline

Why Temporal Intent Detection?Query Temporal Pattern TaxonomyQuery Pattern Detection FrameworkExperiment ResultsApplicationConclusion and Future Work

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 Class QoT OQ AMQ PMQ average

P 0.952 0.928 0.846 0.914 0.910

R 0.973 0.915 0.831 0.924 0.911

F1 0.962 0.922 0.838 0.919 0.910

Classification Performance Comparison for Different Query Categories

AMQ

PMQ

QoT

OQ

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Feature Effectiveness Analysis

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Outline

Why Temporal Intent Detection?Query Temporal Pattern TaxonomyQuery Pattern Detection FrameworkExperiment ResultsApplicationConclusion and Future Work

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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.

Tt Zz Sd

ztqdPqzPqtPqSP ))),,|(1(1)(|()|()|(

Temporal Intent Coverage

Subtopic Coverage

Novelty

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Application – Temporal Diversity

MMR SIGIR’98xQuAD WWW’10IA-Select WSDM’09LM+T+D SIGIR’13RM+T+S+D Our method

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Outline

Why Temporal Intent Detection?Query Temporal Pattern TaxonomyQuery Pattern Detection FrameworkExperiment ResultsApplicationConclusion and Future Work

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.

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Future Work

More Effective FeaturesData sparse problem for long queries

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Thanks a lot for your attention!