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http://www.photoxpress.com/stock-photos/man/blank/card/2694628
Suggestion
Reformulation
     http://www.london-eating.co.uk/newsletter/2006/may/images/wine.jpg
Teaching Math or Something / foundphotoslj
http://www.photoxpress.com/stock-photos/brown/human/white/6458163
• Mei Q, Zhou D, Church K. Query suggestion
 using hitting time. Proceeding of the 17th ACM
 conference on Information and knowledge mining - CIKM '08.
 2008:469.

• Herdagdelen A, Ciaramita M, Mahler D, et al.
 Generalized syntactic and semantic
 models of query reformulation. In:
 Proceedings of the 33rd ACM SIGIR Conference, Geneva,
 Switzerland.; 2010.
Query suggestion
using hitting time
      (CIKM 2008)
introduction (1)
introduction (2)
http://morguefile.com/archive/display/105605
V1   W   V2
P
V1   W   V2
                    w(i, j)
              pij =
                      di
              di =          w(i, j)
                     j∈V2

                          30
                  p11   =
                          52
                          15
                  p12   =
                          52
                           7
                  p13   =
                          52
V1
V1   V2    V1
                               w(i, k) w(k, j)
                pij =
                                 di      dk
                        k∈V2

                   =           pik pkj
                        k∈V2
A V
Xt                 t


                 TA


     A
         T   A
                 = min{t : Xt ∈ A, t ≥ 0}
i


P [T   A
           = m|X0 = i] =         P [X1 = j|X0 = i]
                           j∈V
                                 ·P [T   A
                                             = m − 1|X0 = j]

                      =          pij P [T    A
                                                 = m − 1|X0 = j]
                           j∈V

TA = m          m-1                 A
A
          X0 = i                 TA                hi
          ∞
 A
hi   =         mP [T     A
                             = m|X0 = i]
         m=1
          ∞
     =         m         pij P [T   A
                                        = m − 1|X0 = j]
         m=1       j∈V
               ∞
     =             (m − 1)pij P [T          A
                                                = m − 1|X0 = j]
         j∈V m=1
                             ∞
               +                 pij P [T   A
                                                = m − 1|X0 = j]
                   j∈V m=1
∞
          (m − 1)pij P [T   A
                                = m − 1|X0 = j]
j∈V m=1
                  ∞
          =             npij P [T   A
                                        = n|X0 = j]
              j∈V n=1

                                        =              A
                                                  pij hj
                                            j∈V
∞
       P [T   A
                  = m − 1|X0 = j] = 1
 m=1


      ∞
           pij P [T   A
                          = m − 1|X0 = j] =         pij = 1
j∈V m=1                                       j∈V



  A
 hi    =               A
                  pij hj   +1      A
                                  hi   = 0, f or i ∈ A
           j∈V
w(i, k) w(k, j)
                      pij =
                                       di      dk
                              k∈V2




hi (t + 1) =         pij hj (t) + 1, hi (0) = 0
               j=s
http://www.photoxpress.com/stock-photos/macro/fashion/person/2134644/
http://www.flickr.com/photos/fabricio/50889626
c(QT , U RL, U ser)
p(QT |U RL, U ser) =
                        Q c(Q, U RL, U ser)

                          p(U RL|Q, U ser)
                          ˆ
p(U RL|QT , U ser)p(QT |U ser)
                     ˆ
p(QT |U RL, U ser) =
                            p(U RL|U ser)


                p(QT |U RL, U ser)        k=j   wik
        wij =
                   1 − p(QT |U RL, U ser)

    ˆ
    p
                            4
        p(U RL|Q, IP ) =
        ˆ                        λi p(U RL|Q, IPi )
                           i=0
Generalized syntactic and
  semantic models of
  query reformulation
         (SIGIR 2010)
http://www.photoxpress.com/stock-photos/meeting/recipe/business/1996577/
p(x, y)
    P M I(x, y) = log
                      p(x)p(y)
                     P M I(x, y)
    P M I(J)(x, y) =
                     − log p(x, y)
                 P M I(x, y)
P M I(S)(x, y) =
                 − log p(x)
                 P M I(x, y)
P M I(G)(x, y) =
                 − log(p(y))
“Sorted”
http://morguefile.com/archive/display/105605
Ns            Nt                 Ns,t
p(qs ) =    , p(qt ) =    , p(qs , qt ) =
         N             N                   N
                              P M IW eb (qs , qt )
dA (x, y)




∀a, b ∈ T, cE1 (a, b) = 1 if a = b




∀a, b ∈ T, cE2 (a, b) = dA (a, b)
    if a = N U LL||b = N U LL
s(wi , wj ) = 2 − 2f (wi , wj ) + (wi , wj )


     ∀a, b ∈ T, cGE (a, b) = s(a, b)
      if a = N U LL||b = N U LL
qs = qs qt , qt = qt qs

                    
                    1
                                    if wi = wj ∧ wi ∈ qs ∧ wj ∈ qt
ni,j (qs , qt ) =          1
                                     if wi ∈ qs ∧ wj ∈ qt
                     (|qs ||qt |)
                    
                     0               otherwise

                                           Ni,j =            ni,j (qs , qt )
                                                    qs ,qt
Ni,· =       Ni,j , N·, =       Ni,j , N =         Ni,j
         j                   i                i,j




           Ni,·              N·,j              Ni,j
P (i, ·) =      , P (·, j) =      , P (i, j) =
           N                 N                  N
∀xi                S(yj |xi ) = 1, S(λ|ξ) = 0
      yj ∈T ∪{λ}
m                            n+z
                          G(z)n!z!
p(y|x) =                                           S(yk , xk )
                          (n + z)!
           z=max(0,m−n)              x   y   k=1
http://nix.ksc.nasa.gov/info?id=KSC-00PD-5019&orgid=5
Similarity    Spearman
NN                  0.500     GenEdit(G)            0.380
Oommen-Kashyap      0.470     GenEdit(J)            0.365
DistSim             0.438     SortedEdit2           0.320
Mean all            0.435     SortedEdit1           0.314
SortedGenEdit(S)    0.429     PMIWeb(G)             0.283
SortedGenEdit(G)    0.428     Edit2                 0.270
PMIWeb(S)           0.417     Edit1                 0.252
PMIWeb(J)           0.409     Length-target(Char)   0.139
SortedGenEdit(J)    0.408     Length-target(Term)   0.112
GenEdit(S)          0.382     log-prob target       -0.161
Similarity Function   Spearman   Mean all          0.386
NN                      0.432    PMIWeb(G)         0.369
GenEdit(G)              0.424    PMIWeb(J)         0.330
SortedGenEdit(G)        0.419    DistSim           0.322
GenEdit(S)              0.414    Edit1             0.299
SortedGenEdit(S)        0.407    SortedEdit1       0.298
GenEdit(J)              0.402    Edit2             0.292
Oommen-Kashyap          0.391    SortedEdit2       0.288
SortedGenEdit(J)        0.391    PMIWeb(S)         0.264
                                 log-prob target   0.114
                                 Length-
                                                   -0.036
                                 target(Char)
                                 Length-
                                                   -0.077
                                 target(Term)
Similarity Measure   QS1500    CC2000
Oommen-Kashyap       0.470*   0.391*(6)
SortedGenEdit(S)     0.429*   0.407* (4)
SortedGenEdit(G)     0.428*   0.419* (2)
SortedGenEdit(J)      0.408   0.391* (7)
GenEdit(S)            0.382   0.414* (3)
GenEdit(G)            0.380   0.424* (1)
GenEdit(J)            0.365   0.402* (5)
SortedEdit2           0.320   0.288 (11)
SortedEdit1           0.314    0.298 (9)
Edit2                 0.270   0.292 (10)
Edit1                 0.252    0.299 (8)
http://www.imageafter.com/image.php?image=b3_landscapes008.jpg
“
http://www.flickr.com/photos/walkadog/3560856061/

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Query Suggestion @ tokyotextmining#2

  • 1.
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  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17. Suggestion Reformulation http://www.london-eating.co.uk/newsletter/2006/may/images/wine.jpg
  • 18. Teaching Math or Something / foundphotoslj
  • 19.
  • 20.
  • 22. • Mei Q, Zhou D, Church K. Query suggestion using hitting time. Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08. 2008:469. • Herdagdelen A, Ciaramita M, Mahler D, et al. Generalized syntactic and semantic models of query reformulation. In: Proceedings of the 33rd ACM SIGIR Conference, Geneva, Switzerland.; 2010.
  • 23. Query suggestion using hitting time (CIKM 2008)
  • 24.
  • 28. V1 W V2
  • 29. P V1 W V2 w(i, j) pij = di di = w(i, j) j∈V2 30 p11 = 52 15 p12 = 52 7 p13 = 52
  • 30.
  • 31. V1 V1 V2 V1 w(i, k) w(k, j) pij = di dk k∈V2 = pik pkj k∈V2
  • 32.
  • 33. A V Xt t TA A T A = min{t : Xt ∈ A, t ≥ 0}
  • 34. i P [T A = m|X0 = i] = P [X1 = j|X0 = i] j∈V ·P [T A = m − 1|X0 = j] = pij P [T A = m − 1|X0 = j] j∈V TA = m m-1 A
  • 35. A X0 = i TA hi ∞ A hi = mP [T A = m|X0 = i] m=1 ∞ = m pij P [T A = m − 1|X0 = j] m=1 j∈V ∞ = (m − 1)pij P [T A = m − 1|X0 = j] j∈V m=1 ∞ + pij P [T A = m − 1|X0 = j] j∈V m=1
  • 36. (m − 1)pij P [T A = m − 1|X0 = j] j∈V m=1 ∞ = npij P [T A = n|X0 = j] j∈V n=1 = A pij hj j∈V
  • 37. P [T A = m − 1|X0 = j] = 1 m=1 ∞ pij P [T A = m − 1|X0 = j] = pij = 1 j∈V m=1 j∈V A hi = A pij hj +1 A hi = 0, f or i ∈ A j∈V
  • 38. w(i, k) w(k, j) pij = di dk k∈V2 hi (t + 1) = pij hj (t) + 1, hi (0) = 0 j=s
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 47. c(QT , U RL, U ser) p(QT |U RL, U ser) = Q c(Q, U RL, U ser) p(U RL|Q, U ser) ˆ
  • 48. p(U RL|QT , U ser)p(QT |U ser) ˆ p(QT |U RL, U ser) = p(U RL|U ser) p(QT |U RL, U ser) k=j wik wij = 1 − p(QT |U RL, U ser) ˆ p 4 p(U RL|Q, IP ) = ˆ λi p(U RL|Q, IPi ) i=0
  • 49.
  • 50.
  • 51.
  • 52.
  • 53. Generalized syntactic and semantic models of query reformulation (SIGIR 2010)
  • 54.
  • 56.
  • 57. p(x, y) P M I(x, y) = log p(x)p(y) P M I(x, y) P M I(J)(x, y) = − log p(x, y) P M I(x, y) P M I(S)(x, y) = − log p(x) P M I(x, y) P M I(G)(x, y) = − log(p(y))
  • 60. Ns Nt Ns,t p(qs ) = , p(qt ) = , p(qs , qt ) = N N N P M IW eb (qs , qt )
  • 61. dA (x, y) ∀a, b ∈ T, cE1 (a, b) = 1 if a = b ∀a, b ∈ T, cE2 (a, b) = dA (a, b) if a = N U LL||b = N U LL
  • 62.
  • 63. s(wi , wj ) = 2 − 2f (wi , wj ) + (wi , wj ) ∀a, b ∈ T, cGE (a, b) = s(a, b) if a = N U LL||b = N U LL
  • 64. qs = qs qt , qt = qt qs  1  if wi = wj ∧ wi ∈ qs ∧ wj ∈ qt ni,j (qs , qt ) = 1 if wi ∈ qs ∧ wj ∈ qt  (|qs ||qt |)  0 otherwise Ni,j = ni,j (qs , qt ) qs ,qt
  • 65. Ni,· = Ni,j , N·, = Ni,j , N = Ni,j j i i,j Ni,· N·,j Ni,j P (i, ·) = , P (·, j) = , P (i, j) = N N N
  • 66.
  • 67. ∀xi S(yj |xi ) = 1, S(λ|ξ) = 0 yj ∈T ∪{λ}
  • 68. m n+z G(z)n!z! p(y|x) = S(yk , xk ) (n + z)! z=max(0,m−n) x y k=1
  • 69.
  • 71.
  • 72.
  • 73.
  • 74. Similarity Spearman NN 0.500 GenEdit(G) 0.380 Oommen-Kashyap 0.470 GenEdit(J) 0.365 DistSim 0.438 SortedEdit2 0.320 Mean all 0.435 SortedEdit1 0.314 SortedGenEdit(S) 0.429 PMIWeb(G) 0.283 SortedGenEdit(G) 0.428 Edit2 0.270 PMIWeb(S) 0.417 Edit1 0.252 PMIWeb(J) 0.409 Length-target(Char) 0.139 SortedGenEdit(J) 0.408 Length-target(Term) 0.112 GenEdit(S) 0.382 log-prob target -0.161
  • 75. Similarity Function Spearman Mean all 0.386 NN 0.432 PMIWeb(G) 0.369 GenEdit(G) 0.424 PMIWeb(J) 0.330 SortedGenEdit(G) 0.419 DistSim 0.322 GenEdit(S) 0.414 Edit1 0.299 SortedGenEdit(S) 0.407 SortedEdit1 0.298 GenEdit(J) 0.402 Edit2 0.292 Oommen-Kashyap 0.391 SortedEdit2 0.288 SortedGenEdit(J) 0.391 PMIWeb(S) 0.264 log-prob target 0.114 Length- -0.036 target(Char) Length- -0.077 target(Term)
  • 76. Similarity Measure QS1500 CC2000 Oommen-Kashyap 0.470* 0.391*(6) SortedGenEdit(S) 0.429* 0.407* (4) SortedGenEdit(G) 0.428* 0.419* (2) SortedGenEdit(J) 0.408 0.391* (7) GenEdit(S) 0.382 0.414* (3) GenEdit(G) 0.380 0.424* (1) GenEdit(J) 0.365 0.402* (5) SortedEdit2 0.320 0.288 (11) SortedEdit1 0.314 0.298 (9) Edit2 0.270 0.292 (10) Edit1 0.252 0.299 (8)
  • 77.
  • 78.
  • 80.
  • 81.

Editor's Notes