1. Can we build recommender system for artwork evaluation?
Zbigniew W. Ras & Anna Gelich
KDD Laboratory
Computer Science Department
University of North Carolina, Charlotte
Research Sponsored by

2. Assigning Price Tags for Art - BIG DATA Problem
Semantic WEB –
Very rich source of data/knowledge about artists & Art
Websites with information about artists: ArtPrice (649,129 artists)
National Gallery, Artists Signatures
Websites with information about art:
ArtPrice (detailed auction results, marked trends)
Saatchiart, Artlog, DevianArt (26 milion members,
1.5 milion comments daily), …..
Recommender
System
Partitioning Artists into
Semantically Similar Buckets &
treating buckets as a WEB Crowd
(1) Data Collection:
Extracting Information from WEB using crawlers,
spiders [Apache Nutch based on Apache Hadoop]
Data Processing:
Extracting Features including Price (discretization cuts:
480, 1020, 2335, 5100, 10650, 19500, …)
(3) Data Mining /Map Reduce/

3. x2 Sample Attributes: Has to be discretized
[0,480), [480,1020), [1020,2335), [2335,5100),
[5100,10650), [10650,19500), [19500, -)
Price discretized
Dense area
Sparse area
480
1020
2335
5100
10650
19500 x2 x2 x2 x2 x2
Price Distribution
20,543 paintings
Price range: 0 – 40,000
Has to be discretized

4. Confusion Matrix f a b d e c g  classified as Number 1863 731 264 71
f = [0,480)
2933
646
2401
1054
384 53 3
a = [480, 1020)
4543
250
1060
2568
1236
134 12 8
b = [1020, 2335)
5268
109
374
1135
2915
430 41 15
d = [2335, 5100)
5019 26 88
229
822
676 73 25
e = [5100, 10650)
1939 6 14 57
166
123 78 9
c = [10650, 19500)
453 4 18 85 44 11
214
g = [19500, -)
388
It looks like paintings within the range $5,100 – $19,500 are overpriced
Decision Tree Classifier (Precision)
Price Range
Correctness
Left Ext. Correctness
Right Ext. Correctness
Overpriced (?)
[0,480)
64% -
88%
[480, 1020)
53%
67%
76% NO
[1020, 2335)
49%
69%
72%
[2335, 5100)
58%
81%
YES
[5110, 10650)
35%
77%
39%
[10650, 19500)
17%
44%
19%
[19500, -)
55%
Average Precision - 52%; Left Ext Average Precision – 71% ; Right Ext Average Precision – 69%

5. Confusion Matrix f a b d e c g  classified as Number 1863 731 264 71
f = [0,480)
2933
646
2401
1054
384 53 3
a = [480, 1020)
4543
250
1060
2568
1236
134 12 8
b = [1020, 2335)
5268
109
374
1135
2915
430 41 15
d = [2335, 5100)
5019 26 88
229
822
676 73 25
e = [5100, 10650)
1939 6 14 57
166
123 78 9
c = [10650, 19500)
453 4 18 85 44 11
214
g = [19500, -)
388
It looks like paintings within the range $5,100 – $19,500 are overpriced
Decision Tree Classifier (Precision)
Price Range
Correctness
Left Ext. Correctness
Right Ext. Correctness
Overpriced (?)
[0,480)
64% -
88%
[480, 1020)
53%
67%
76% NO
[1020, 2335)
49%
69%
72%
[2335, 5100)
58%
81%
YES
[5110, 10650)
35%
77%
39%
[10650, 19500)
17%
44%
19%
[19500, -)
55%
Average Precision - 52%; Left Ext Average Precision – 71% ; Right Ext Average Precision – 69%

6. Confusion Matrix f a b d e c g  classified as Number 1857 676 286 9911 3 1
f = [0,480)
2933
576
2467
1026
400 61 6 7
a = [480, 1020)
4543
229
982
2668
1175
184 21 9
b = [1020, 2335)
5268 77
336
986
3063
493 43
d = [2335, 5100)
5019 19 76
227
709
812 70 26
e = [5100, 10650)
1939 12 49
151
118
106
c = [10650, 19500)
453 5 18 36 13
239
g = [19500, -)
388
Random Forest (Precision)
Price Range
Correctness
Left Ext. Correctness
Right Ext. Correctness
Overpriced (?)
[0,480)
63% -
86%
[480, 1020)
54%
67%
77% NO
[1020, 2335)
51%
69%
73%
[2335, 5100)
61%
81%
71%
YES
[5110, 10650)
42%
78%
45%
[10650, 19500)
23%
49%
26%
[19500, -)
62%
65%
Average Precision - 55%, Left Ext Average Precision – 71% ; Right Ext Average Precision – 71%

7. Confusion Matrix f a b d e c g  classified as Number 1857 676 286 9911 3 1
f = [0,480)
2933
576
2467
1026
400 61 6 7
a = [480, 1020)
4543
229
982
2668
1175
184 21 9
b = [1020, 2335)
5268 77
336
986
3063
493 43
d = [2335, 5100)
5019 19 76
227
709
812 70 26
e = [5100, 10650)
1939 12 49
151
118
106
c = [10650, 19500)
453 5 18 36 13
239
g = [19500, -)
388
Random Forest (Precision)
Price Range
Correctness
Left Ext. Correctness
Right Ext. Correctness
Overpriced (?)
[0,480)
63% -
86%
[480, 1020)
54%
67%
77% NO
[1020, 2335)
51%
69%
73%
[2335, 5100)
61%
81%
71%
YES
[5110, 10650)
42%
78%
45%
[10650, 19500)
23%
49%
26%
[19500, -)
62%
65%
Average Precision - 55%, Left Ext Average Precision – 71% ; Right Ext Average Precision – 71%

8. WEB (Source of Data & Knowledge)
Extracting artworks (paintings),
relevant features, comments
about artworks, artists biographies
Step 1
Step 1
Initial Dataset of Artists
Initial Dataset of Artworks
Converting data to a format ready
for mining. New features construction
using text mining, sentiment mining,
guided folksonomy.
Step 2
Step 2
Dataset of Artists
Dataset of Artworks
Clustering based on
semantic distance
Step 3
Step 4
For each bucket, identifying
artworks done by artists listed
in that bucket
Disjoint Buckets of Semantically Similar Artists
Collection of Datasets of Artworks
Data Mining and
Crowd Construction
Step 5
Recommender Systems for Artwork Evaluation and Pricing
System Demo (One RS)
System Interface

9. Q & A