Lecture 3 Modeling.pdf

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Retrieval: Ad hoc and Filtering• Ad hoc retrieval– Documents remain relatively static while new queriesare submitted• Filtering– Queries remain relatively static while new documentscome into the system• e.g., news wiring services in the stock market– User profile describes the user’s preferences• Filtering task indicates to the user which document might beinterested to him• Which ones are really relevant is fully reserved to the user– Routing: a variation of filtering• Ranking filtered documents and show this ranking to users3-8
User profile• Simplistic approach– The profile is described through a set ofkeywords– The user provides the necessary keywords• Elaborate approach– Collect information from the user– initial profile + relevance feedback (relevantinformation and nonrelevant information)3-9
Page 1 and 2: Lecture 3 Modeling3-1
Page 3 and 4: Information Retrieval Models• Cla
Page 5 and 6: Taxonomy of Information Retrieval M
Page 7: Combinations of these issuesUSERTAS
Page 11 and 12: Formal Definition of IR Models(cont
Page 13 and 14: Boolean Model• The index term wei
Page 15 and 16: Boolean Model (Continued)• advant
Page 17 and 18: Document representation in vector s
Page 19 and 20: Similarity Measure (Continued)• v
Page 21 and 22: Sample query-documentsimilarity com
Page 23 and 24: Vector Model• w i,j : a positive,
Page 25 and 26: document ranking• Similarity (i.e
Page 27 and 28: Definition of tf• N: total number
Page 29 and 30: Analysis of vector model• advanta
Page 31 and 32: Probabilistic Model (Continued)•
Page 33 and 34: Probabilistic Principle• w i,j
Page 35 and 36: 3-35independence assumption ofindex
Page 37 and 38: Initial guess• P(k i |R) is const
Page 39 and 40: Small values of V and V i• altern
Page 41 and 42: • Initial GuessProbabilistic Mode
Page 43 and 44: No Interaction with User• Retriev
Page 45 and 46: No Interaction with User• Retriev
Page 47 and 48: Interaction with User• Alternativ
Page 49 and 50: Analysis of Probabilistic Model•
Page 51 and 52: Alternative Set Theoretic Models-Fu
Page 53 and 54: Examples• Assume U={d 1 , d 2 , d
Page 55 and 56: Fuzzy OR3-55
Page 57 and 58: Fuzzy Information Retrieval• basi
Page 59 and 60:
Fuzzy Information Retrieval(Continu
Page 61 and 62:
ExampleQuery q=k a ∧ (k b ∨¬k
Page 63 and 64:
Fuzzy Set Model3-63
Page 65 and 66:
Fuzzy Set Model• Sim(q,d): Altern
Page 67 and 68:
Review• Two vectors u and v are l
Page 69 and 70:
Generalized Vector Space Model(Cont
Page 71 and 72:
t=3cccminterm m r m r vectorm 1 =(0
Page 73 and 74:
Generalized Vector Space Model(Cont
Page 75 and 76:
3-7523,823,623,423,283,863,63,423,2
Page 77 and 78:
asic idea• Map each document and
Page 79 and 80:
Singular Value DecompositionA∈R(1
Page 81 and 82:
A=QDQtAQ=tQDQ Q=QDwhere Q=[ q q21Kq
Page 83 and 84:
M=tM= ( K S D= ( DSDSM M=K St22: do
Page 85 and 86:
Ranking in LSI• query: a pseudo-d
Page 87 and 88:
Non-Overlapping Lists• divide the
Page 89 and 90:
Inverted Files• File is represent
Page 91 and 92:
Inverted-file process (Continued)
Page 93 and 94:
Extensions of Inverted Index Operat
Page 95 and 96:
Model Based on Proximal Nodes• hi
Page 97 and 98:
Model Based on Proximal Nodes(Conti
Page 99 and 100:
Models for Browsing• Flat organiz
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