Violet Styler

Ultra-Personalized Custom Service Implementation for Violet-Server

What is Violet Styler?

Violet Styler is a data analyzer to provide users with personalized data.

Features

Burst Time

1946094
 0.8, 0.5, 0.9, 0.8, 1.7, 2.8, 1.9, 1.2, 1.3, 0.9, 0.7, 0.5, 1.3, 1.2, 1.1, 0.7, 0.9, 0.9, 0.8, 0.8, 0.9, 1.1, 1.5, 1.7, 1.2, 0.8, 1.3, 1.0, 0.8, 1.2, 1.3, 0.9, 0.5, 0.7, 0.9, 0.5, 0.9, 1.8, 0.1, 0.0, 0.0
1948960
 0.9, 1.2, 1.6, 1.9, 1.8, 1.6, 1.5, 2.0, 1.8, 1.4, 1.3, 1.4, 1.6, 1.4, 1.7, 1.5, 0.5, 0.6, 1.1, 0.7, 1.2, 1.0, 1.1, 0.7, 0.7, 0.7, 0.6, 0.7, 0.8, 1.2, 1.0, 0.4, 0.3, 0.5, 0.3, 1.0, 1.5, 2.4, 1.5, 1.1, 1.3, 0.8, 1.1, 1.2, 0.7, 0.9, 0.7, 0.8, 0.6, 0.8, 0.6, 0.7, 0.5, 0.5, 0.4, 0.6, 0.4, 0.5, 0.7, 1.2, 0.7, 0.9, 0.9, 0.8, 0.7, 0.8, 0.4, 0.8, 1.3, 2.4, 0.1
1947825
 2.3, 1.2, 1.4, 1.2, 0.8, 1.7, 1.9, 0.8, 1.0, 1.1, 1.2, 0.8, 0.6, 0.5, 0.8, 0.8, 0.5, 0.8, 0.5, 0.4, 0.5, 1.3, 1.0, 0.8, 1.8, 0.0
1947008
 1.8, 1.7, 1.5, 1.5, 1.1, 1.1, 0.8, 0.9, 0.7, 0.5, 1.0, 0.5, 1.2, 1.9, 1.1, 1.3, 1.3, 0.9, 0.5, 0.3, 0.2, 0.4, 1.7, 0.0
1946213
 2.2, 1.8, 1.5, 1.2, 1.2, 1.0, 0.7, 0.6, 0.9, 0.9, 1.0, 0.7, 1.0, 0.8, 0.8, 0.5, 0.5, 0.6, 0.4, 1.6, 1.9, 0.0
1945434
 3.4, 2.0, 1.9, 2.4, 2.8, 1.7, 1.2, 0.9, 0.8, 1.1, 1.6, 1.8, 1.7, 1.4, 1.2, 1.1, 1.7, 2.2, 1.0, 0.6, 1.0, 1.3, 1.1, 0.8, 0.7, 0.7, 1.2, 1.1, 0.7, 0.6, 1.1, 2.1, 1.7, 1.0, 0.9, 0.5, 0.6, 0.7, 0.6, 0.5, 0.5, 0.5, 0.4, 0.3, 0.4, 0.4, 0.4, 0.6, 0.7, 0.6, 0.7, 0.6, 0.6, 0.6, 0.4, 0.3, 0.2, 0.1, 0.7, 0.0, 0.0
1942740
 4.9, 3.9, 3.0, 1.9, 1.5, 2.6, 2.9, 1.9, 1.7, 1.3, 1.5, 1.3, 2.1, 1.5, 1.2, 1.7, 2.8, 1.3, 1.2, 1.1, 1.3, 1.2, 1.1, 1.0, 1.4, 1.7, 1.1, 1.8, 2.8, 2.4, 2.3, 2.9, 2.9, 1.1, 1.3, 2.5, 1.6, 1.4, 1.3, 2.7, 1.4, 1.0, 0.9, 1.2, 0.8, 0.7, 0.8, 0.7, 0.5, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.6, 0.0
1945287
 2.0, 1.4, 1.4, 1.2, 1.2, 1.3, 1.0, 1.5, 0.8, 0.8, 1.2, 1.1, 1.0, 1.0, 1.4, 1.9, 1.3, 1.0, 0.7, 0.9, 0.4, 0.5, 0.7, 0.7, 0.9, 1.1, 0.7, 0.6, 0.6, 1.2, 1.1, 1.3, 1.2, 0.5, 0.6, 0.6, 0.6, 0.6, 0.5, 0.4, 0.3, 0.4, 0.5, 0.7, 0.9, 1.3, 1.4, 2.3, 1.6, 2.1, 1.3, 0.0
1944305
 1.9, 1.7, 1.9, 2.3, 3.1, 2.3, 1.7, 1.2, 0.8, 0.9, 1.0, 1.1, 1.0, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.7, 0.6, 1.1, 0.6, 0.5, 0.4, 0.5, 0.7, 0.4, 0.4, 0.3, 0.7, 1.1, 0.0
1944094
 1.5, 1.1, 1.3, 1.1, 1.2, 1.2, 1.0, 0.6, 0.6, 1.5, 0.0
1948755
 2.3, 1.6, 1.5, 1.6, 1.4, 1.1, 1.2, 1.6, 1.6, 1.1, 1.1, 1.5, 1.3, 1.2, 1.6, 1.7, 1.0, 0.8, 0.8, 1.0, 0.9, 0.9, 0.5, 0.6, 0.6, 0.4, 0.4, 0.6, 1.2, 0.6, 0.4, 0.4, 0.3, 0.3, 0.4, 0.7, 1.2, 1.5, 0.0
...

Personal Read Pattern Analysis

Thumbnail Aggro Index (TAI)

Valid Read Time per Pages (VRTP)

VRTP = VRT / VRP
VRT := Valid Read Time
VRP := Valid Read Page

Drop Out Filter (DOF)

DOF(Array) = Returns an array with the normal distribution 0% to 5% and 95% to 100% are removed.

MPP & V(MPP), V(MPP)'s Singularity

MPP = P x VT ( Page x Valid Time )
V(MPP) = VT x COUNT(P)

Reproduced Valid Read-Time per Pages (RVRTP)

RVRTP = VAvg( V(MPP) )

VRP with Concentration Weight (FVRP)

FVRP = Applies correction values to values between the normal distribution 0% and 20% and 80% to 100% .

Noise Filter Model (NFM)

                   1
nfm(w) = ----------------------
         1 + e^-((w - 0.2) * 5)

User Article Score (Score)

Score := nfm(vavg(mpp) / pages) * fvrp

...
----------------------------
1154251, 46, 63500ms, 1380.4ms, 1522.0, 46/49
50700ms, 1152.3ms, 1071.2, 44/49, 1349.9796571805266
----------------------------
1221893, 40, 59800ms, 1533.3ms, 1342.9, 39/64
45000ms, 1250.0ms, 951.2, 36/64, 1351.889235878271
----------------------------
1809473, 47, 56300ms, 1759.4ms, 1417.7, 32/71
40500ms, 1396.6ms, 885.7, 29/71, 1351.9940779711249
----------------------------
1906183, 185, 264800ms, 1463.0ms, 1750.6, 181/262
204100ms, 1179.8ms, 1050.8, 173/262, 1352.9949475513167
----------------------------
1613612, 27, 33900ms, 1412.5ms, 1573.0, 24/29
22300ms, 1013.6ms, 870.4, 22/29, 1357.5651516140024
----------------------------
1933860, 58, 76700ms, 1475.0ms, 1763.1, 52/76
53400ms, 1089.8ms, 728.2, 49/76, 1359.1061930239641
----------------------------
1962319, 25, 34700ms, 1388.0ms, 1512.7, 25/26
23100ms, 1004.3ms, 795.9, 23/26, 1359.2911665467584
----------------------------
1898387, 32, 44900ms, 1403.1ms, 1024.8, 32/33
39500ms, 1274.2ms, 743.1, 31/33, 1365.4113343788645
----------------------------
1960738, 87, 151100ms, 1820.5ms, 1702.1, 83/186
121100ms, 1552.6ms, 1369.4, 78/186, 1366.7125769557317
----------------------------
1872642, 86, 121500ms, 1396.6ms, 1311.4, 87/87
103900ms, 1236.9ms, 946.4, 84/87, 1368.3342974231996
----------------------------
1339881, 59, 82700ms, 1401.7ms, 1922.6, 59/60
59400ms, 1060.7ms, 1028.1, 56/60, 1376.3851899085778
...

User Confidence Level (UCL)

Likes and Dislikes Index (LDI)

Article Relevance Tree

Article Read Time per Page (ARTPP)

Plot (Read Count, RTPP)

Plot (Read Count, RTPP-Std)

Plot (Read Count, RTPP-Std divide by Sqrt(Read Count))