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ACM CIKM 2008, Oct , Napa Valley 1 Mining Term Association Patterns from Search Logs for Effective Query Reformulation Xuanhui Wang and ChengXiang Zhai Department of Computer Science University of Illinois at Urbana-Champaign

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ACM CIKM 2008, Oct , Napa Valley 2 Ineffective Queries reduce space command latex

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ACM CIKM 2008, Oct , Napa Valley 3 Effective Queries squeeze space command latex

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ACM CIKM 2008, Oct , Napa Valley 4 More Examples If you want to wash your vehicle –vehicle wash, auto wash –car wash, truck wash If you want to buy a car –auto quotes –auto sale quotes? –auto insurance quotes?

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ACM CIKM 2008, Oct , Napa Valley 5 What Makes a Query Ineffective? Vocabulary mismatch –reduce space command latex vs squeeze space command latex –auto wash vs car wash Lack of discrimination –auto quotes vs auto sale quotes … How can we help improving ineffective queries? Term substitution Term addition

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ACM CIKM 2008, Oct , Napa Valley 6 Our Contribution We cast query reformulation as term level pattern mining from search logs We define two basic types of patterns at term level and propose probabilistic methods –Context-sensitive term substitution auto car | _wash, car auto | _trade –Context-sensitive term addition +sale | auto_quotes We evaluate our methods on commercial search engine logs and show their effectiveness

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ACM CIKM 2008, Oct , Napa Valley 7 Problem Formulation Query Collection Task 1: Contextual Models Task 2: Translation Models q = auto wash Task 3: Pattern Mining auto car | _wash auto truck | _wash +southland | _auto wash … Patterns Search logs Offline partOnline part car wash truck wash southland auto wash …

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ACM CIKM 2008, Oct , Napa Valley 8 Task 1: Contextual Models enterprise car rental rental car budget car rental car pricing car pictures car accidents … G: General context Syntagmatic relations Capture terms frequently co-occur with w inside queries Sample query collection rental: enterprise: budget: pricing: … Model P G ( * |car)

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ACM CIKM 2008, Oct , Napa Valley 9 Task 1: Contextual Models enterprise car rental rental car budget car rental car pricing car pictures car accidents … Model: P L1 ( * | car) Syntagmatic relations Capture terms frequently co-occur with w inside queries Sample query collection rental: enterprise: budget: … L 1 : 1 st Left Context

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ACM CIKM 2008, Oct , Napa Valley 10 Task 1: Contextual Models enterprise car rental rental car budget car rental car pricing car pictures car accidents … Model: P R1 ( * |w) Syntagmatic relations Capture terms frequently co-occur with w inside queries Sample query collection rental: 0.4 pricing: 0.2 pictures: 0.2 accidents: 0.2 … R 1 : 1 st Right context

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ACM CIKM 2008, Oct , Napa Valley 11 Task 2: Translation Models Paradigmatic relations (car and auto) Capture terms that are substitutable with w Similar contexts high translation probability Translation models Probability of generating ss context from ws contextual model Size of L 1 contextSize of R 1 context

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ACM CIKM 2008, Oct , Napa Valley 12 Task 3.1: Pattern Mining–Term Substitution q=[w 1 …w i-1 w i w i+1 …w n ] q=[w 1 …w i-1 sw i+1 …w n ] Substitute w i by s Which word s should be chosen? Local factor Global factor: translation model

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ACM CIKM 2008, Oct , Napa Valley 13 Estimating Local Factor Independence w 1 …w i-1 __w i+1 …w n s …… Ignore those terms far away

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ACM CIKM 2008, Oct , Napa Valley 14 Task 3.2: Pattern Mining–Term Addition q=[w 1 …w i-1 w i …w n ] q=[w 1 …w i-1 rw i …w n ] Adding r before w i Similar to the Local Factor in Term Substitution Patterns Uniform

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ACM CIKM 2008, Oct , Napa Valley 15 Evaluation: Data Preparation From Microsoft Live Labs 5/1/2006 5/31/20065/20/2006 History Logs Future logs History Collection 4.4M queries 1.6M are distinct 1.3M user sessions Used to construct test cases

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ACM CIKM 2008, Oct , Napa Valley 16 Examples of Contextual Models Left and Right contexts are different General context mixed them together

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ACM CIKM 2008, Oct , Napa Valley 17 Examples of Translation Models Conceptually similar keywords have high translation probabilities Provide possibility for exploratory search in an interactive manner

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ACM CIKM 2008, Oct , Napa Valley 18 Examples of Term Substitution Substitution is context sensitive Intuitively, reworded queries are more effective

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ACM CIKM 2008, Oct , Napa Valley 19 Effectiveness Comparison of Term Substitution – Experiment Design Q1Q1 Q2Q2 QkQk R 21 R 22 R 23 … R k1 R k2 R k3 … C3C3 C2C2 C1C1 Session … … How well can a reformulated query rank C 1, C 2, and C 3 on the top? Q1Q1 reformulation Q 1 dxC3C1C2dx…dxC3C1C2dx… Q 2 Q 3 dxC1dxdxdx…dxC1dxdxdx… dxC2dxC3dx…dxC2dxC3dx… Best

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ACM CIKM 2008, Oct , Napa Valley 20 Results Our method reformulates queries more effectively [Jones06] Our method #Recommended Queries

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ACM CIKM 2008, Oct , Napa Valley 21 Term Addition Patterns Term addition patterns can refine a broad query

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ACM CIKM 2008, Oct , Napa Valley 22 Related Work Query suggestions [e.g., Jones06, Sahami et al06] –Discover pattern at query level –Rely on external resources or training data –Does not consider the effectiveness Query modifications in IR [Rocchio71, Anick03] –Expand queries from returned documents –Does not rely on search logs, mostly adding terms Related work in NLP community [Lin98, Rapp02] –Finding synonym or near synonyms –Syntagmatic and paradigmatic relations –Not used for query reformulation

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ACM CIKM 2008, Oct , Napa Valley 23 Conclusions and Future Work We propose a new way to mine search logs for patterns to address ineffective queries –Vocabulary mismatch –Lack of discrimination We define and mine two basic patterns at term level –Context-sensitive term substitution patterns –Context-sensitive term addition patterns Experiments show the effectiveness of our methods In the future, –Use relevance judgments instead of clicks –Exploit click information for better query reformulation

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ACM CIKM 2008, Oct , Napa Valley 24 Thank You!

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ACM CIKM 2008, Oct , Napa Valley 25 Offline Efficiency Linear scalability with data size More data

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ACM CIKM 2008, Oct , Napa Valley 26 Enhancement by User Sessions Improve translation models by user sessions –t(express|idol) is very high –american express and american idol are frequent Method w=idol top N thresholding t(idols|idol)=1 Normalized Mutual Information

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ACM CIKM 2008, Oct , Napa Valley 27 Formal Definitions Query is a sequence of keywords –q = [w 1 w 2 …w n ] Context-sensitive term substitution –[w w|c L _c R ] Context-sensitive term addition –[+w|c L _c R ] Query rewording: replace a word w i by s –q = [w 1 …w i-1 w i w i+1 …w n ] q = [w 1 …w i-1 sw i+1 …w n ] Query refinement: add a new word r –q = [w 1 …w i w i+1 …w n ] q = [w 1 …w i rw i+1 …w n ]

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