Learning to rank for information retrieval:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2011
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XVII, 285 S. graph. Darst. 235 mm x 155 mm |
| ISBN: | 9783642142666 9783642142673 |
Staff View
MARC
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Record in the Search Index
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| adam_text |
IMAGE 1
CONTENTS
PART I OVERVIEW OF LEARNING TO RANK 1 INTRODUCTION 3
1.1 OVERVIEW 3
1.2 RANKING IN INFORMATION RETRIEVAL 7
1.2.1 CONVENTIONAL RANKING MODELS 7
1.2.2 QUERY-LEVEL POSITION-BASED EVALUATIONS 11
1.3 LEARNING TO RANK 15
1.3.1 MACHINE LEARNING FRAMEWORK 16
1.3.2 DEFINITION OF LEARNING TO RANK 17
1.3.3 LEARNING-TO-RANK FRAMEWORK 18
1.4 BOOK OVERVIEW 23
1.5 EXERCISES 24
REFERENCES 25
PART II MAJOR APPROACHES TO LEARNING TO RANK
2 THE POINTWISE APPROACH 33
2.1 OVERVIEW 33
2.2 REGRESSION-BASED ALGORITHMS 33
2.2.1 SUBSET RANKING WITH REGRESSION 34
2.3 CLASSIFICATION-BASED ALGORITHMS 35
2.3.1 BINARY CLASSIFICATION FOR RANKING 35
2.3.2 MULTI-CLASS CLASSIFICATION FOR RANKING 37
2.4 ORDINAL REGRESSION-BASED ALGORITHMS 39
2.4.1 PERCEPTRON-BASED RANKING (PRANKING) 39
2.4.2 RANKING WITH LARGE MARGIN PRINCIPLES 40
2.4.3 ORDINAL REGRESSION WITH THRESHOLD-BASED LOSS FUNCTIONS . 42 2.5
DISCUSSIONS 42
2.5.1 RELATIONSHIP WITH RELEVANCE FEEDBACK 43
2.5.2 PROBLEMS WITH THE POINTWISE APPROACH 44
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/100313646X
DIGITALISIERT DURCH
IMAGE 2
XUE CONTENTS
2.5.3 IMPROVED ALGORITHMS 44
2.6 SUMMARY 45
2.7 EXERCISES 45
REFERENCES 46
3 THE PAIRWISE APPROACH 49
3.1 OVERVIEW 49
3.2 EXAMPLE ALGORITHMS 49
3.2.1 ORDERING WITH PREFERENCE FUNCTION 49
3.2.2 SORTNET: NEURAL NETWORK-BASED SORTING ALGORITHM . . . 51 3.2.3
RANKNET: LEARNING TO RANK WITH GRADIENT DESCENT 52 3.2.4 FRANK: RANKING
WITH A FIDELITY LOSS 53
3.2.5 RANKBOOST 54
3.2.6 RANKING SVM 56
3.2.7 GBRANK 58
3.3 IMPROVED ALGORITHMS 59
3.3.1 MULTIPLE HYPERPLANE RANKER 59
3.3.2 MAGNITUDE-PRESERVING RANKING 60
3.3.3 IR-SVM 61
3.3.4 ROBUST PAIRWISE RANKING WITH SIGMOID FUNCTIONS 62 3.3.5 P-NORMPUSH
63
3.3.6 ORDERED WEIGHTED AVERAGE FOR RANKING 64
3.3.7 LAMBDARANK 65
3.3.8 ROBUST SPARSE RANKER 66
3.4 SUMMARY 67
3.5 EXERCISES 67
REFERENCES 68
4 THE LISTWISE APPROACH 71
4.1 OVERVIEW 71
4.2 MINIMIZATION OF MEASURE-SPECIFIC LOSS 72
4.2.1 MEASURE APPROXIMATION 72
4.2.2 BOUND OPTIMIZATION 77
4.2.3 NON-SMOOTH OPTIMIZATION 78
4.2.4 DISCUSSIONS 80
4.3 MINIMIZATION OF NON-MEASURE-SPECIFIC LOSS 80
4.3.1 LISTNET 81
4.3.2 LISTMLE 82
4.3.3 RANKING USING CUMULATIVE DISTRIBUTION NETWORKS 83 4.3.4 BOLTZRANK
84
4.4 SUMMARY 85
4.5 EXERCISES 86
REFERENCES 87
5 ANALYSIS OF THE APPROACHES 89
5.1 OVERVIEW 89
5.2 THE POINTWISE APPROACH 89
IMAGE 3
CONTENTS XIUE
5.3 THE PAIRWISE APPROACH 91
5.4 THE LISTWISE APPROACH 94
5.4.1 NON-MEASURE-SPECIFIC LOSS 94
5.4.2 MEASURE-SPECIFIC LOSS 95
5.5 SUMMARY 98
5.6 EXERCISES 98
REFERENCES 99
PART III ADVANCED TOPICS IN LEARNING TO RANK
6 RELATIONAL RANKING 103
6.1 GENERAL RELATIONAL RANKING FRAMEWORK 104
6.1.1 RELATIONAL RANKING SVM 104
6.1.2 CONTINUOUS CONDITIONAL RANDOM FIELDS 106
6.2 LEARNING DIVERSE RANKING 107
6.3 DISCUSSIONS 110
REFERENCES I LL
7 QUERY-DEPENDENT RANKING 113
7.1 QUERY-DEPENDENT LOSS FUNCTION 113
7.2 QUERY-DEPENDENT RANKING FUNCTION 115
7.2.1 QUERY CLASSIFICATION-BASED APPROACH 115
7.2.2 K NEAREST NEIGHBOR-BASED APPROACH 116
7.2.3 QUERY CLUSTERING-BASED APPROACH 118
7.2.4 TWO-LAYER LEARNING APPROACH 119
7.3 DISCUSSIONS 120
REFERENCES 121
8 SEMI-SUPERVISED RANKING 123
8.1 INDUCTIVE APPROACH 123
8.2 TRANSDUCTIVE APPROACH 124
8.3 DISCUSSIONS 125
REFERENCES 125
9 TRANSFER RANKING 127
9.1 FEATURE-LEVEL TRANSFER RANKING 128
9.2 INSTANCE-LEVEL TRANSFER RANKING 128
9.3 DISCUSSIONS 130
REFERENCES 130
PART IV BENCHMARK DATASETS FOR LEARNING TO RANK
10 THE LETOR DATASETS 133
10.1 OVERVIEW 133
10.2 DOCUMENT CORPORA 133
10.2.1 THE "GOV" CORPUS AND SIX QUERY SETS 134
10.2.2 THE OHSUMED CORPUS 134
10.2.3 THE "GOV2" CORPUS AND TWO QUERY SETS 135
IMAGE 4
XIV CONTENTS
10.3 DOCUMENT SAMPLING 135
10.4 FEATURE EXTRACTION 136
10.5 META INFORMATION 136
10.6 LEARNING TASKS 138
10.7 DISCUSSIONS 142
REFERENCES 142
11 EXPERIMENTAL RESULTS ON LETOR 145
11.1 EXPERIMENTAL SETTINGS 145
11.2 EXPERIMENTAL RESULTS ON LETOR 3.0 146
11.3 EXPERIMENTAL RESULTS ON LETOR 4.0 149
11.4 DISCUSSIONS 150
11.5 EXERCISES 151
REFERENCES 151
12 OTHER DATASETS 153
12.1 YAHOO! LEARNING-TO-RANK CHALLENGE DATASETS 153
12.2 MICROSOFT LEARNING-TO-RANK DATASETS 154
12.3 DISCUSSIONS 155
REFERENCES 155
PART V PRACTICAL ISSUES IN LEARNING TO RANK
13 DATA PREPROCESSING FOR LEARNING TO RANK 159
13.1 OVERVIEW 159
13.2 GROUND TRUTH MINING FROM LOGS 160
13.2.1 USER CLICK MODELS 160
13.2.2 CLICK DATA ENHANCEMENT 166
13.3 TRAINING DATA SELECTION 168
13.3.1 DOCUMENT AND QUERY SELECTION FOR LABELING 169 13.3.2 DOCUMENT AND
QUERY SELECTION FOR TRAINING 171 13.3.3 FEATURE SELECTION FOR TRAINING
175
13.4 SUMMARY 176
13.5 EXERCISES 176
REFERENCES 177
14 APPLICATIONS OF LEARNING TO RANK 181
14.1 OVERVIEW 181
14.2 QUESTION ANSWERING 181
14.2.1 DEFINITIONAL QA 182
14.2.2 QUANTITY CONSENSUS QA 183
14.2.3 NON-FACTOID QA 184
14.2.4 WHYQA 185
14.3 MULTIMEDIA RETRIEVAL 186
14.4 TEXT SUMMARIZATION 187
14.5 ONLINE ADVERTISING 188
14.6 SUMMARY 189
IMAGE 5
CONTENTS XV
14.7 EXERCISES 190
REFERENCES 190
PART VI THEORIES IN LEARNING TO RANK
15 STATISTICAL LEARNING THEORY FOR RANKING 195
15.1 OVERVIEW 195
15.2 STATISTICAL LEARNING THEORY 195
15.3 LEARNING THEORY FOR RANKING 197
15.3.1 STATISTICAL RANKING FRAMEWORK 197
15.3.2 GENERALIZATION ANALYSIS FOR RANKING 198
15.3.3 STATISTICAL CONSISTENCY FOR RANKING 198
15.4 EXERCISES 199
REFERENCES 199
16 STATISTICAL RANKING FRAMEWORK 201
16.1 DOCUMENT RANKING FRAMEWORK 202
16.1.1 THE POINTWISE APPROACH 202
16.1.2 THE PAIRWISE APPROACH 202
16.1.3 THE LISTWISE APPROACH 204
16.2 SUBSET RANKING FRAMEWORK 204
16.2.1 THE POINTWISE APPROACH 205
16.2.2 THE PAIRWISE APPROACH 205
16.2.3 THE LISTWISE APPROACH 206
16.3 TWO-LAYER RANKING FRAMEWORK 206
16.3.1 THE POINTWISE APPROACH 206
16.3.2 THE PAIRWISE APPROACH 207
16.3.3 THE LISTWISE APPROACH 208
16.4 SUMMARY 208
16.5 EXERCISES 208
REFERENCES 209
17 GENERALIZATION ANALYSIS FOR RANKING 211
17.1 OVERVIEW 211
17.2 UNIFORM GENERALIZATION BOUNDS FOR RANKING 212
17.2.1 FOR DOCUMENT RANKING 212
17.2.2 FOR SUBSET RANKING 214
17.2.3 FOR TWO-LAYER RANKING 216
17.3 ALGORITHM-DEPENDENT GENERALIZATION BOUND 217
17.3.1 FOR DOCUMENT RANKING 218
17.3.2 FOR SUBSET RANKING 219
17.3.3 FOR TWO-LAYER RANKING 220
17.4 SUMMARY 220
17.5 EXERCISES 221
REFERENCES 221
IMAGE 6
XVI CONTENTS
18 STATISTICAL CONSISTENCY FOR RANKING 223
18.1 OVERVIEW 223
18.2 CONSISTENCY ANALYSIS FOR DOCUMENT RANKING 224
18.2.1 REGARDING PAIRWISE 0-1 LOSS 224
18.3 CONSISTENCY ANALYSIS FOR SUBSET RANKING 224
18.3.1 REGARDING DCG-BASED RANKING ERROR 225
18.3.2 REGARDING PERMUTATION-LEVEL 0-1 LOSS 225
18.3.3 REGARDING TOP-JT TRUE LOSS 226
18.3.4 REGARDING WEIGHTED KENDALL'S T 227
18.4 CONSISTENCY ANALYSIS FOR TWO-LAYER RANKING 229
18.5 SUMMARY 229
18.6 EXERCISES 230
REFERENCES 230
PART VII SUMMARY AND OUTLOOK
19 SUMMARY 235
REFERENCES 238
20 FUTURE WORK 241
20.1 SAMPLE SELECTION BIAS 241
20.2 DIRECT LEARNING FROM LOGS 242
20.3 FEATURE ENGINEERING 243
20.4 ADVANCED RANKING MODELS 243
20.5 LARGE-SCALE LEARNING TO RANK 244
20.6 ONLINE COMPLEXITY VERSUS ACCURACY 245
20.7 ROBUST LEARNING TO RANK 245
20.8 ONLINE LEARNING TO RANK 246
20.9 BEYOND RANKING 247
REFERENCES 247
PART VIII APPENDIX
21 MATHEMATICAL BACKGROUND 251
21.1 PROBABILITY THEORY 251
21.1.1 PROBABILITY SPACE AND RANDOM VARIABLES 251
21.1.2 PROBABILITY DISTRIBUTIONS 252
21.1.3 EXPECTATIONS AND VARIANCES 254
21.2 LINEAR ALGEBRA AND MATRIX COMPUTATION 255
21.2.1 NOTATIONS 255
21.2.2 BASIC MATRIX OPERATIONS AND PROPERTIES 256
21.2.3 EIGENVALUES AND EIGENVECTORS 261
21.3 CONVEX OPTIMIZATION 262
21.3.1 CONVEX SET AND CONVEX FUNCTION 262
21.3.2 CONDITIONS FOR CONVEXITY 263
21.3.3 CONVEX OPTIMIZATION PROBLEM 263
21.3.4 LAGRANGIAN DUALITY 264
IMAGE 7
CONTENTS XVUE
21.3.5 KKT CONDITIONS 265
REFERENCES 266
22 MACHINE LEARNING 267
22.1 REGRESSION 267
22.1.1 LINEAR REGRESSION 267
22.1.2 PROBABILISTIC EXPLANATION 268
22.2 CLASSIFICATION 269
22.2.1 NEURAL NETWORKS 270
22.2.2 SUPPORT VECTOR MACHINES 271
22.2.3 BOOSTING 273
22.2.4 K NEAREST NEIGHBOR (KNN) 274
22.3 STATISTICAL LEARNING THEORY 274
22.3.1 FORMALIZATION 275
22.3.2 BOUNDS FOR \R(G) - R(G)\ 277
REFERENCES 282
INDEX 283 |
| any_adam_object | 1 |
| author | Liu, Tie-Yan 1976- |
| author_GND | (DE-588)1011995492 |
| author_facet | Liu, Tie-Yan 1976- |
| author_role | aut |
| author_sort | Liu, Tie-Yan 1976- |
| author_variant | t y l tyl |
| building | Verbundindex |
| bvnumber | BV036762795 |
| classification_rvk | AN 95000 ST 270 |
| ctrlnum | (OCoLC)705950396 (DE-599)DNB100313646X |
| dewey-full | 025.04252 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 025 - Operations of libraries and archives |
| dewey-raw | 025.04252 |
| dewey-search | 025.04252 |
| dewey-sort | 225.04252 |
| dewey-tens | 020 - Library and information sciences |
| discipline | Allgemeines Informatik |
| format | Book |
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| id | DE-604.BV036762795 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T07:09:44Z |
| institution | BVB |
| isbn | 9783642142666 9783642142673 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020679800 |
| oclc_num | 705950396 |
| open_access_boolean | |
| owner | DE-863 DE-BY-FWS DE-20 DE-11 DE-92 |
| owner_facet | DE-863 DE-BY-FWS DE-20 DE-11 DE-92 |
| physical | XVII, 285 S. graph. Darst. 235 mm x 155 mm |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Liu, Tie-Yan 1976- Learning to rank for information retrieval Information Retrieval (DE-588)4072803-1 gnd Ranking (DE-588)4307945-3 gnd Suchmaschine (DE-588)4423007-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4072803-1 (DE-588)4307945-3 (DE-588)4423007-2 (DE-588)4193754-5 |
| title | Learning to rank for information retrieval |
| title_auth | Learning to rank for information retrieval |
| title_exact_search | Learning to rank for information retrieval |
| title_full | Learning to rank for information retrieval Tie-Yan Liu |
| title_fullStr | Learning to rank for information retrieval Tie-Yan Liu |
| title_full_unstemmed | Learning to rank for information retrieval Tie-Yan Liu |
| title_short | Learning to rank for information retrieval |
| title_sort | learning to rank for information retrieval |
| topic | Information Retrieval (DE-588)4072803-1 gnd Ranking (DE-588)4307945-3 gnd Suchmaschine (DE-588)4423007-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Information Retrieval Ranking Suchmaschine Maschinelles Lernen |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3487299&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020679800&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT liutieyan learningtorankforinformationretrieval |
Description
THWS Würzburg Teilbibliothek SHL, Raum I.2.11
| Call Number: |
1340 ST 270 L783 |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |