Ebay Data Set

Here you can find auction data collected from eBay and used for evaluating second price auctions with reserve. The original data set was kindly donated to us by Jay Grossman . The features used can be found in the website above.
raw.tar.gz

The data set used in [1] has been formatted to define the highest and second highest bid
mod.tar.gz
This data set also includes user information missing from the raw data set. Unless indicated otherwise all features are binary. "Category-feature" represents membership to one of the categories described in the original data set . Each line is truncated to its last non-zero entryEach line is truncated to its last non-zero entry.

0: 'Highest Bid - continuous '
1: 'Second Highest Bid - continuous '
2: 'RegistrationSite_Canada'
3: 'Category-feature'
4: 'FeedbackScore - continuous'
5: 'SellerBusinessType_Private'
6: 'FeedbackRatingStar_Green'
7: 'Category-feature'
8: 'PositiveFeedbackPercent -continuous '
9: 'Category-feature'
10: 'Authenticated'
11: 'Status_Confirmed'
12: 'FeedbackPrivate'
13: 'NewUser'
14: 'isHOF'
15: 'SellerBusinessType_Undefined'
16: 'Category-feature'
17: 'Category-feature'
18: 'Category-feature'
19: 'TopRatedSeller'
20: 'RegistrationSite_US'
21: 'Category-feature'
22: 'FeedbackRatingStar_Red'
23: 'Category-feature'
24: 'FeedbackRatingStar_TurquoiseShooting'
25: 'Category-feature'
26: 'Category-feature'
27: 'Category-feature'
28: 'FeedbackRatingStar_YellowShooting'
29: 'Category-feature'
30: 'Category-feature'
31: 'FeedbackRatingStar_Yellow'
32: 'Category-feature'
33: 'Category-feature'
34: 'Category-feature'
35: 'Category-feature'
36: 'Category-feature'
37: 'Category-feature'
38: 'Category-feature'
39: 'Category-feature'
40: 'Category-feature'
41: 'FeedbackRatingStar_Turquoise'
42: 'Category-feature'
43: 'Category-feature'
44: 'FeedbackRatingStar_Purple'
45: 'Category-feature'
46: 'Status_Suspended'
47: 'Category-feature'
48: 'Category-feature'
49: 'Category-feature'
50: 'FeedbackRatingStar_PurpleShooting'
51: 'Category-feature'
52: 'Category-feature'
53: 'Category-feature'
54: 'SellerBusinessType_Commercial'
55: 'Category-feature'
56: 'Category-feature'
57: 'Category-feature'
58: 'Category-feature'
59: 'Category-feature'
60: 'Category-feature'
61: 'Category-feature'
62: 'Category-feature'
63: 'Category-feature'
64: 'FeedbackRatingStar_RedShooting'
65: 'Category-feature'
66: 'Category-feature'
67: 'FeedbackRatingStar_Blue'
68: 'RegistrationSite_CanadaFrench'
69: 'Category-feature'
70: 'Status_AccountOnHold'
71: 'RegistrationSite_UK'
72: 'FeedbackRatingStar_None'
73: 'Category-feature'
74: 'Category-feature'
75: 'Status_TermPending'
76: 'RegistrationSite_HongKong'
77: 'RegistrationSite_Italy'
78: 'RegistrationSite_Germany'
79: 'RegistrationSite_Philippines'

[1] Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve.
Mehryar Mohri and Andrés Muñoz Medina. In Proceedings of ICML 2014