The document characterizes user behavior across live TV, video on demand (VOD), and catch-up TV in a large-scale cable TV environment, highlighting significant differences in usage patterns. Live TV garners the highest viewership and engagement, yet VOD and catch-up TV also accumulate substantial viewing hours, necessitating tailored recommendation systems. Users display preferences for content types based on time of day and show histories, emphasizing the need for adaptive recommendation strategies.

1. A Large-Scale Characterization of
User Behaviour in Cable TV
Diogo Gonçalves, Miguel Costa, Francisco Couto
LaSIGE @ Faculty of Sciences, University of Lisbon
RecSysTV 2016, Boston, USA
September 15, 2016

2. • Today, there are many services from which to choose contents to watch:
• Live TV
• Video on Demand (VOD)
• Catch-up TV
• Over-the-top (OTT) from 3rd parties (e.g. Netflix)
• Understanding how users interact with such services is important to increase:
• user satisfaction
• user engagement
• user consumption
(this knowledge helps to enhance the recommendation systems of Cable TV providers)
• We didn’t find any study comparing usage patterns between Live TV, VOD and Catch-up TV
in an integrated way from a large scale Cable TV operator.
2

3. • Live-TV
• a client can watch any video content that is being broadcast live (e.g. a live
soccer game).
• VOD (video-on-demand)
• a client can watch any video content anytime that was pre-recorded and made
available, usually a movie or series.
• Catch-up TV
• is a type of VOD, where a client can watch any video content that was broadcast
live up to a few days before (e.g. up to 7 days).
Contents are delivered via Set-Top Boxes (STB) installed in users‘ homes.
3

4. Live Catch-up VOD Total
Users 896,000 806,000 220,000 897,000
Programs 24,000 24,000 15,000 39,000
Episodes 330,000 330,000 15,000 345,000
Programs per moment (avg.) 160 6,000 15,000 21,000
Episodes per moment (avg.) 160 35,000 15,000 50,000
Views (>10 min) 617,000,000 56,000,000 9,000,000 682,000,000
User views (avg.) 688 70 40 758
User views per month (avg.) 327 33 19 360
October to December 2015 (9 weeks)
160 channels available to the user
4

5. Some
Results
5

6. Number of users per day
0
100
200
300
400
500
600
700
1 8 15 22 29 36 43 50 57
#users(thousands)
Days
Catch-up TV Live TV VOD
Live TV has much
more users than
Catch-up and VOD
together, but less
research.
Should we focus more in
improving the user
experience for Live TV?
6

7. 0
2
4
6
8
10
12
14
16
18
1 8 15 22 29 36 43 50 57
#views(millions)
Days
Catch-up TV Live TV VOD
Number of views per day Number of watched hours per day
Live TV has much more views and watched
hours than VOD & Catch-up
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1 8 15 22 29 36 43 50 57
#hours(millions)
Days
Catch-up TV Live TV VOD
7

8. 0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
00
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
#views(millions)
hour of day
Live TV Catch-up TV VOD
Number of views per hour
The graph shows
the typical work/rest
cycle of users for the
3 TV services.
sleeping
working
@home
8

9. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
#views
% program watched
Live TV Catch-up TV VOD
The average percentage of content watched in
Live TV is small (27%) when compared
with Catch-up TV (50%) and VOD (60%)
stop watching
sooner
most people
do not watch
the full content
9

10. 0%
20%
40%
60%
80%
100%
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
#views
% program watched
News TV Series Entertainment
Kids Documentaries Sports
Movies Adults
kids category
was more watched
adults category
was less watched
Live TV
10

11. Distribution of program types per
day made available by the Cable TV operator
Distribution of program types
watched by users on live and catch-up TV
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 8 15 22 29 36 43 50 57
#programs
Days
Recurring Programs (Repeats)
Recurring Programs (New Episodes)
New Programs
0%
20%
40%
60%
80%
100%
1 8 15 22 29 36 43 50 57
#programs
Days
Recurring Programs, Recurring for User, Repeat
Recurring Programs, Recurring for User, New Episode
Recurring Programs, New for user
New programs
~20% of watched episodes are repeated.
These are mostly kids programs.most users watch
new episodes
of programs already seen
not having feedback
from the specific user11

12. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
#views
hour of day
News TV Series Entertainment
Kids Documentaries Sports
Views in Catch-up TV per category over the day
users watch different
categories in different hours
12

13. 0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
0 24 48 72 96 120 144 168
#views(millions)
hours after broadcast
Views in Catch-up TV
most users watch contents
from the last hour 13

14. • The characterization of Live TV, Catch-up TV and VOD on a large-scale Cable TV provider shows
usage differences between these 3 services. These insights enable to adapt and create better
recommendation algorithms depending on the TV service.
• Live TV receives the majority of views. Still, Catch-up TV and VOD accumulate a large amount of
views and hours watched. The 3 services should have recommendations.
• Users tend to watch Catch-up TV and VOD programs for longer, when compared to Live TV. The
implicit feedback provided by the program views should be adjusted to the TV service.
• Users tend to watch some categories (e.g. Kids) for longer than others (e.g. Adults). The implicit
feedback provided by the program views should be adjusted to the content categories.
• Users prefer to watch different types of programs depending on the hour of day. We should adjust
the recommendations for the time period.
• Users prefer to watch new episodes of programs previously watched by them. Should we
recommend the programs that users usually watch or should we suggest new programs?
• Users prefer to watch programs recently broadcast in Catch-up TV. Should we recommend the most
recent contents first?
14

15. Thank you.
migcosta@gmail.com