take-away tv: recharging work commutes with greedy and predictive preloading of tv content
TRANSCRIPT
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Mining TV-on-demand Services
EPSRC project
Dmytro Karamshuk
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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
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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)
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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
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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
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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
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A useful insight: users watch across networks
Users complete watching across different sessions and networks
Fixed-line ISPs Mobile ISPs
Per user completion ratio
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Speculative Content Pre-fetching
Pre-fetch at home Watch during commutes
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Speculative Content Pre-fetching
Not very efficient…
Per-user mobile savings with pre-fetching
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Can we do better with predictive preloading?
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Towards Predicting User PreferencesFeatured content
Most Popular Content
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How important are UI guidance?
For 20% of users > 60% of their access are from the Front Page
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Content Types
11 channels
11 categories and 172 genres
thousands shows
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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.
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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
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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
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Accuracy of Personalized Predictions
For 50% of users over 70% chance of fitting in Top-10 predictions
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When do we do predictions?
Front Pages are updated over night…
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When do we do predictions?
… and remain largely unchanged for 24h
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How much traffic can be saved?
Predictive pre-fetching can potentially save near 71% of mobile usage
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We made mobile users happy!How about the rest?
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Access PatternsAverage per-user # sessions Correlation with Internet speed
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Content Delivery for Home Broadband
Install more distributed caches
May requires significant investments
Any alternatives?
Problem: how to handle peak load from 32M users
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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
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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
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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
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Dmytro KaramshukKing’s College London
“True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information” -
Winston Churchill