Managing advertising campaigns — an approximate planning approach
Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Girgin, Sertan [verfasserIn] Mary, Jérémie [verfasserIn] Preux, Philippe [verfasserIn] Nicol, Olivier [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2012 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Frontiers of computer science in China - Beijing : Higher Education Press, 2007, 6(2012), 2 vom: 31. März, Seite 209-229 |
|---|---|
| Übergeordnetes Werk: |
volume:6 ; year:2012 ; number:2 ; day:31 ; month:03 ; pages:209-229 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11704-012-2873-5 |
|---|
| Katalog-ID: |
SPR021935920 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR021935920 | ||
| 003 | DE-627 | ||
| 005 | 20220111090302.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201006s2012 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11704-012-2873-5 |2 doi | |
| 035 | |a (DE-627)SPR021935920 | ||
| 035 | |a (SPR)s11704-012-2873-5-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q ASE |
| 084 | |a 54.00 |2 bkl | ||
| 100 | 1 | |a Girgin, Sertan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Managing advertising campaigns — an approximate planning approach |
| 264 | 1 | |c 2012 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. | ||
| 650 | 4 | |a advertisement selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a web sites |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a non-stationary setting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a linear programming |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a multi-arm bandit |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a click-through rate (CTR) estimation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a exploration-exploitation trade-off |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mary, Jérémie |e verfasserin |4 aut | |
| 700 | 1 | |a Preux, Philippe |e verfasserin |4 aut | |
| 700 | 1 | |a Nicol, Olivier |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Frontiers of computer science in China |d Beijing : Higher Education Press, 2007 |g 6(2012), 2 vom: 31. März, Seite 209-229 |w (DE-627)545787726 |w (DE-600)2388878-7 |x 1673-7466 |7 nnns |
| 773 | 1 | 8 | |g volume:6 |g year:2012 |g number:2 |g day:31 |g month:03 |g pages:209-229 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s11704-012-2873-5 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_101 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_138 | ||
| 912 | |a GBV_ILN_152 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_187 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_250 | ||
| 912 | |a GBV_ILN_281 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2005 | ||
| 936 | b | k | |a 54.00 |q ASE |
| 951 | |a AR | ||
| 952 | |d 6 |j 2012 |e 2 |b 31 |c 03 |h 209-229 | ||
| author_variant |
s g sg j m jm p p pp o n on |
|---|---|
| matchkey_str |
article:16737466:2012----::aaigdetsncmagsnprxmtp |
| hierarchy_sort_str |
2012 |
| bklnumber |
54.00 |
| publishDate |
2012 |
| allfields |
10.1007/s11704-012-2873-5 doi (DE-627)SPR021935920 (SPR)s11704-012-2873-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Girgin, Sertan verfasserin aut Managing advertising campaigns — an approximate planning approach 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. advertisement selection (dpeaa)DE-He213 web sites (dpeaa)DE-He213 optimization (dpeaa)DE-He213 non-stationary setting (dpeaa)DE-He213 linear programming (dpeaa)DE-He213 multi-arm bandit (dpeaa)DE-He213 click-through rate (CTR) estimation (dpeaa)DE-He213 exploration-exploitation trade-off (dpeaa)DE-He213 Mary, Jérémie verfasserin aut Preux, Philippe verfasserin aut Nicol, Olivier verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 6(2012), 2 vom: 31. März, Seite 209-229 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:6 year:2012 number:2 day:31 month:03 pages:209-229 https://dx.doi.org/10.1007/s11704-012-2873-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 6 2012 2 31 03 209-229 |
| spelling |
10.1007/s11704-012-2873-5 doi (DE-627)SPR021935920 (SPR)s11704-012-2873-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Girgin, Sertan verfasserin aut Managing advertising campaigns — an approximate planning approach 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. advertisement selection (dpeaa)DE-He213 web sites (dpeaa)DE-He213 optimization (dpeaa)DE-He213 non-stationary setting (dpeaa)DE-He213 linear programming (dpeaa)DE-He213 multi-arm bandit (dpeaa)DE-He213 click-through rate (CTR) estimation (dpeaa)DE-He213 exploration-exploitation trade-off (dpeaa)DE-He213 Mary, Jérémie verfasserin aut Preux, Philippe verfasserin aut Nicol, Olivier verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 6(2012), 2 vom: 31. März, Seite 209-229 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:6 year:2012 number:2 day:31 month:03 pages:209-229 https://dx.doi.org/10.1007/s11704-012-2873-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 6 2012 2 31 03 209-229 |
| allfields_unstemmed |
10.1007/s11704-012-2873-5 doi (DE-627)SPR021935920 (SPR)s11704-012-2873-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Girgin, Sertan verfasserin aut Managing advertising campaigns — an approximate planning approach 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. advertisement selection (dpeaa)DE-He213 web sites (dpeaa)DE-He213 optimization (dpeaa)DE-He213 non-stationary setting (dpeaa)DE-He213 linear programming (dpeaa)DE-He213 multi-arm bandit (dpeaa)DE-He213 click-through rate (CTR) estimation (dpeaa)DE-He213 exploration-exploitation trade-off (dpeaa)DE-He213 Mary, Jérémie verfasserin aut Preux, Philippe verfasserin aut Nicol, Olivier verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 6(2012), 2 vom: 31. März, Seite 209-229 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:6 year:2012 number:2 day:31 month:03 pages:209-229 https://dx.doi.org/10.1007/s11704-012-2873-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 6 2012 2 31 03 209-229 |
| allfieldsGer |
10.1007/s11704-012-2873-5 doi (DE-627)SPR021935920 (SPR)s11704-012-2873-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Girgin, Sertan verfasserin aut Managing advertising campaigns — an approximate planning approach 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. advertisement selection (dpeaa)DE-He213 web sites (dpeaa)DE-He213 optimization (dpeaa)DE-He213 non-stationary setting (dpeaa)DE-He213 linear programming (dpeaa)DE-He213 multi-arm bandit (dpeaa)DE-He213 click-through rate (CTR) estimation (dpeaa)DE-He213 exploration-exploitation trade-off (dpeaa)DE-He213 Mary, Jérémie verfasserin aut Preux, Philippe verfasserin aut Nicol, Olivier verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 6(2012), 2 vom: 31. März, Seite 209-229 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:6 year:2012 number:2 day:31 month:03 pages:209-229 https://dx.doi.org/10.1007/s11704-012-2873-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 6 2012 2 31 03 209-229 |
| allfieldsSound |
10.1007/s11704-012-2873-5 doi (DE-627)SPR021935920 (SPR)s11704-012-2873-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Girgin, Sertan verfasserin aut Managing advertising campaigns — an approximate planning approach 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. advertisement selection (dpeaa)DE-He213 web sites (dpeaa)DE-He213 optimization (dpeaa)DE-He213 non-stationary setting (dpeaa)DE-He213 linear programming (dpeaa)DE-He213 multi-arm bandit (dpeaa)DE-He213 click-through rate (CTR) estimation (dpeaa)DE-He213 exploration-exploitation trade-off (dpeaa)DE-He213 Mary, Jérémie verfasserin aut Preux, Philippe verfasserin aut Nicol, Olivier verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 6(2012), 2 vom: 31. März, Seite 209-229 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:6 year:2012 number:2 day:31 month:03 pages:209-229 https://dx.doi.org/10.1007/s11704-012-2873-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 6 2012 2 31 03 209-229 |
| language |
English |
| source |
Enthalten in Frontiers of computer science in China 6(2012), 2 vom: 31. März, Seite 209-229 volume:6 year:2012 number:2 day:31 month:03 pages:209-229 |
| sourceStr |
Enthalten in Frontiers of computer science in China 6(2012), 2 vom: 31. März, Seite 209-229 volume:6 year:2012 number:2 day:31 month:03 pages:209-229 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
advertisement selection web sites optimization non-stationary setting linear programming multi-arm bandit click-through rate (CTR) estimation exploration-exploitation trade-off |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Frontiers of computer science in China |
| authorswithroles_txt_mv |
Girgin, Sertan @@aut@@ Mary, Jérémie @@aut@@ Preux, Philippe @@aut@@ Nicol, Olivier @@aut@@ |
| publishDateDaySort_date |
2012-03-31T00:00:00Z |
| hierarchy_top_id |
545787726 |
| dewey-sort |
14 |
| id |
SPR021935920 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR021935920</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111090302.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11704-012-2873-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR021935920</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11704-012-2873-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Girgin, Sertan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Managing advertising campaigns — an approximate planning approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">advertisement selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">web sites</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">non-stationary setting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">linear programming</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-arm bandit</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">click-through rate (CTR) estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">exploration-exploitation trade-off</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mary, Jérémie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Preux, Philippe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicol, Olivier</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Frontiers of computer science in China</subfield><subfield code="d">Beijing : Higher Education Press, 2007</subfield><subfield code="g">6(2012), 2 vom: 31. März, Seite 209-229</subfield><subfield code="w">(DE-627)545787726</subfield><subfield code="w">(DE-600)2388878-7</subfield><subfield code="x">1673-7466</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:2</subfield><subfield code="g">day:31</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:209-229</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11704-012-2873-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2012</subfield><subfield code="e">2</subfield><subfield code="b">31</subfield><subfield code="c">03</subfield><subfield code="h">209-229</subfield></datafield></record></collection>
|
| author |
Girgin, Sertan |
| spellingShingle |
Girgin, Sertan ddc 004 bkl 54.00 misc advertisement selection misc web sites misc optimization misc non-stationary setting misc linear programming misc multi-arm bandit misc click-through rate (CTR) estimation misc exploration-exploitation trade-off Managing advertising campaigns — an approximate planning approach |
| authorStr |
Girgin, Sertan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)545787726 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1673-7466 |
| topic_title |
004 ASE 54.00 bkl Managing advertising campaigns — an approximate planning approach advertisement selection (dpeaa)DE-He213 web sites (dpeaa)DE-He213 optimization (dpeaa)DE-He213 non-stationary setting (dpeaa)DE-He213 linear programming (dpeaa)DE-He213 multi-arm bandit (dpeaa)DE-He213 click-through rate (CTR) estimation (dpeaa)DE-He213 exploration-exploitation trade-off (dpeaa)DE-He213 |
| topic |
ddc 004 bkl 54.00 misc advertisement selection misc web sites misc optimization misc non-stationary setting misc linear programming misc multi-arm bandit misc click-through rate (CTR) estimation misc exploration-exploitation trade-off |
| topic_unstemmed |
ddc 004 bkl 54.00 misc advertisement selection misc web sites misc optimization misc non-stationary setting misc linear programming misc multi-arm bandit misc click-through rate (CTR) estimation misc exploration-exploitation trade-off |
| topic_browse |
ddc 004 bkl 54.00 misc advertisement selection misc web sites misc optimization misc non-stationary setting misc linear programming misc multi-arm bandit misc click-through rate (CTR) estimation misc exploration-exploitation trade-off |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Frontiers of computer science in China |
| hierarchy_parent_id |
545787726 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Frontiers of computer science in China |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)545787726 (DE-600)2388878-7 |
| title |
Managing advertising campaigns — an approximate planning approach |
| ctrlnum |
(DE-627)SPR021935920 (SPR)s11704-012-2873-5-e |
| title_full |
Managing advertising campaigns — an approximate planning approach |
| author_sort |
Girgin, Sertan |
| journal |
Frontiers of computer science in China |
| journalStr |
Frontiers of computer science in China |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2012 |
| contenttype_str_mv |
txt |
| container_start_page |
209 |
| author_browse |
Girgin, Sertan Mary, Jérémie Preux, Philippe Nicol, Olivier |
| container_volume |
6 |
| class |
004 ASE 54.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Girgin, Sertan |
| doi_str_mv |
10.1007/s11704-012-2873-5 |
| dewey-full |
004 |
| author2-role |
verfasserin |
| title_sort |
managing advertising campaigns — an approximate planning approach |
| title_auth |
Managing advertising campaigns — an approximate planning approach |
| abstract |
Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. |
| abstractGer |
Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. |
| abstract_unstemmed |
Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 |
| container_issue |
2 |
| title_short |
Managing advertising campaigns — an approximate planning approach |
| url |
https://dx.doi.org/10.1007/s11704-012-2873-5 |
| remote_bool |
true |
| author2 |
Mary, Jérémie Preux, Philippe Nicol, Olivier |
| author2Str |
Mary, Jérémie Preux, Philippe Nicol, Olivier |
| ppnlink |
545787726 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11704-012-2873-5 |
| up_date |
2024-07-04T01:05:46.602Z |
| _version_ |
1803608545407533056 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR021935920</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111090302.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11704-012-2873-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR021935920</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11704-012-2873-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Girgin, Sertan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Managing advertising campaigns — an approximate planning approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We consider the problem of displaying commercial advertisements on web pages, in the “cost per click” model. The advertisement server has to learn the appeal of each type of visitor for the different advertisements in order to maximize the profit. Advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of $ 10^{−4} $. Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones.We introduce NOSEED, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate the performance of our system through simulations on a realistic model designed with an important commercial web actor.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">advertisement selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">web sites</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">non-stationary setting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">linear programming</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-arm bandit</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">click-through rate (CTR) estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">exploration-exploitation trade-off</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mary, Jérémie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Preux, Philippe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicol, Olivier</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Frontiers of computer science in China</subfield><subfield code="d">Beijing : Higher Education Press, 2007</subfield><subfield code="g">6(2012), 2 vom: 31. März, Seite 209-229</subfield><subfield code="w">(DE-627)545787726</subfield><subfield code="w">(DE-600)2388878-7</subfield><subfield code="x">1673-7466</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:2</subfield><subfield code="g">day:31</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:209-229</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11704-012-2873-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2012</subfield><subfield code="e">2</subfield><subfield code="b">31</subfield><subfield code="c">03</subfield><subfield code="h">209-229</subfield></datafield></record></collection>
|
| score |
7.401634 |