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Exploring the Query-Flow Graph with a Mixture Model for Query Recommendation. Lu Bai , Jiafeng Guo , Xueqi Cheng, Xiubo Geng , Pan Du. Institute of Computing Technology , CAS. Outline. Introduction Our approach Experimental results Conclusion & Future work. Introduction.

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exploring the query flow graph with a mixture model for query recommendation

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

Lu Bai, JiafengGuo, Xueqi Cheng, XiuboGeng, Pan Du

Institute of Computing Technology , CAS

outline
Outline
  • Introduction
  • Our approach
  • Experimental results
  • Conclusion & Future work
introduction
Introduction
  • Query recommendation
    • Generated from web query log
    • Different types of information are considered, including search results, clickthrough data, search sessions.
introduction4
Introduction
  • Recently, query-flow graph was introduced into query recommendation.

Yahoo  360

Yahoo  Yahoo mail

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360  Xbox 360  kinect

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1

1

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Kinect Xbox 720

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Yahoo messenger  Yahoo

Yahoo mail  Yahoo messenger

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1

360  Xbox 360  Xbox 720

apple  Yahoo

apple  apple tree

introduction5
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

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1

1

1

1

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1

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our w ork
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.
probabilistic model of generating query flow graph
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.
probabilistic model of generating query flow graph8
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

probabilistic model of generating query flow graph9
Probabilistic model of generating query-flow graph
  • EM algorithm is used to estimate parameters
    • E step
    • M step
intent biased random walk
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

experiments
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.
e xperiments
Experiments
  • Learning performance on different intent number.
experiments13
Experiments
  • Learned query intents:
experiments14
Experiments
  • Dangling query suggestion
  • Ambiguous query suggestion
experiments15
Experiments
  • Performance improvement based on user click behaviors
conclusion and future work
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.