Mining TV-on-demand Services

EPSRC project

Dmytro Karamshuk

Users - 32 M/month

IP address – 20 M/month

Sessions - 1.9 Billion

May 2013 – Jan 2014

≈ 50% of population

Large-scale study of BBC iPlayer

UK Population – 64M

2  x  INFOCOM’2015,  ToN’2015,  JSAC’2016

Longitudinal View across ISPs

Fixed-line Internet market (5 representative providers)

Mobile market is more dynamic than the fixed-line Internet market

Mobile Internet market (5 representative providers)

Data caps decrease market share

All-you-can-eat data(M1, M5)

Limited-cap data packages(M2 – M4)

All-you-can-eat plans boost user consumption

Temporal Patterns in different ISPsFixed-line accesses (F1-F5) peaks

in the evening hours

Mobile users watch more during commutes

Fixe

d Li

ned

ISPs

Mob

ile, l

imite

d da

ta

caps

There is a problem…

Internet on trains in the UK is no good

A study shows that 23.2% 3G packets and 37.2% 4G packets on the major train routes failed

A useful insight: users watch across networks

Users complete watching across different sessions and networks

Fixed-line ISPs Mobile ISPs

Per user completion ratio

Speculative Content Pre-fetching

Pre-fetch at home Watch during commutes

Speculative Content Pre-fetching

Not very efficient…

Per-user mobile savings with pre-fetching

Can we do better with predictive preloading?

Towards Predicting User PreferencesFeatured content

Most Popular Content

How important are UI guidance?

For 20% of users > 60% of their access are from the Front Page

Content Types

11 channels

11 categories and 172 genres

thousands shows

1 channel 2 channels 3 channels

20%0% 40% 60% 100%

1 category 2 category 3 categories

30%0% 75%55% 100%

1 genre 2 gen. 3 gen.

15%0% 40% 50%30%

4 gen.

100%

1 sh. 2 sh. 3

10%0% 25%20%

4 sh.

100%35%

User Focus on Different Content Types

share of users with all their sessions from:

out of 11 channels

out of 171 genres

out of thousands shows

out of 11 categ.

importancecontent category 0.038

content genre 0.063 category affinity 0.042

genre affinity 0.103show affinity 0.179

channel affinity 0.043 content age 0.087

User PreferencesTotal importance: 0.555

importancefeatured content 0.061

featured position 0.061

content popularity rank 0.071

popularity position 0.008

featured probability 0.091

UI GuidanceTotal importance: 0.292

importancepreviously watched 0.066

completion ratio 0.081 probability of re-watching 0.007

Repeatedly Watched ContentTotal importance: 0.154

Engineering Features

Supervised Learning

Problem: For a given user U and an episode E predict whether U will watch E

Binary Classification Problem f(U,E) -> {0,1}

Random Forest: fast, good performance on high dimensional data

Negative Examples: randomly sample from what users did not watch

Predictions: Predict probability, rank all episodes by probability

Accuracy of Personalized Predictions

For 50% of users over 70% chance of fitting in Top-10 predictions

When do we do predictions?

Front Pages are updated over night…

When do we do predictions?

… and remain largely unchanged for 24h

How much traffic can be saved?

Predictive pre-fetching can potentially save near 71% of mobile usage

We made mobile users happy!How about the rest?

Access PatternsAverage per-user # sessions Correlation with Internet speed

Content Delivery for Home Broadband

Install more distributed caches

May requires significant investments

Any alternatives?

Problem: how to handle peak load from 32M users

Alternative: Peer-assisted Content Delivery

Content Serversuser

user user

user

useruser

average of 5K users online every sec in the first day after release

5K duplicates every second!!!

Ask users for assistance

Elegant Theoretical Model for very Complex Behavior

around 88% of savings can be achieved

Data AnalysisTh

eore

tical

Mod

el

G c 1 e c

Why it works?

Top-5% of the content corpus accounts for 80% of traffic

Most of accesses happen in the first day after release

Yes, it’s all about very popular content

Dmytro KaramshukKing’s College London

“True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information” -

Winston Churchill