1. …real time CRM
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 1
Recommendation engine
Extension to CRM system
Brief introduction
© Invite CRM, Ltd., 06/2012

2. Suggested architecture
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 2
Rec
engine
Rec
engine
B2C
CRM
B2C
CRM
customer DBcustomer DB
other
browsed
web pages
other
browsed
web pages personalised web
content
segmented
campaigns
service model
customer
behaviour
tracking
recommendationscustomer
behaviour
tracking
customer
profiles
customer
profiles
dashed lines reflect optional
elements of the architecture

3. Recommendation engine options
• Invite team has considered several options for recommendation functionality
– searching for optimum cost/performance output
• According to our experience following options can be considered
1. Advanced segmentation used for personalised content and recommendations
2. Optimised user interface enriched with recommendation algorithm
3. Universal media recommendation solution
• Within specification, feasibility study Invite team is ready to analyse further
cost/performance optimum option
• Recommendation algorithm and universal recommendation engine options
would be tested on sample data
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 3

4. 1. Advanced segmentation – architecture
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 4
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
B2C
CRM
B2C
CRMcustomer DBcustomer DB
All customer behaviour data will be used to build
customer database creating rich segmentation model
Based on most relevant segments customer will
receive:
1.personalised Voyo content and web pages layout
2.personalised content recommendations

5. 1. Advanced segmentation – methodics
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 5
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
genre actors browsing
Hollywood local
home
page
P1 P2films serials
actio
n
roma
ntic

6. 1. Advanced segmentation – methodics
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 6
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
genre actors browsing
Hollywood local
home
page
P1 P2films serials
actio
n
roma
ntic
IIIIIIIII IIIIIIIII IIIIIIIIII
IIIIIIII II IIIIIIIIII IIIIIII IIIIIIIIIII II
IIIII II

7. 1. Advanced segmentation – valuation
• Pros
– Relatively narrow theme
compared to full scale e-shop
(amazon) – films, serials… -
customer decision models are not
so comprehensive
– Relatively easy to implement
– Favourable pricing model
– Satisfactory results – increase in
sales 10-30%
• Cons
– Missing comprehensive, universal
and „self-learning“ behavioural
models
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 7

8. 2. Recommendation algorithm – architecture
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 8
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
B2C
CRM
B2C
CRMcustomer DBcustomer DB
Rec algorithmRec algorithm
Tailor made recommendation algorithm with self-
learning capabilities based on customer DB and fully
integrated into Voyo user interface will suggest
personalised content to users.
Recommendation algorithm may be used for customer
profiling for the use of CRM system for the consequent
segmentation, campaigns and service model.

9. 2. Recommendation algorithm – methodics
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 9
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
genre actors browsing
Hollywood local
home
page
P1 P2films serials
actio
n
roma
ntic
IIIIIIIII IIIIIIIII IIIIIIIIII
IIIIIIII II IIIIIIIIII IIIIIII IIIIIIIIIII II
IIIII II

10. 2. Recommendation algorithm – methodics
• There are 2 basic methods for automated recommendation – collaborative
filtering and content-based recommendation.
• In case of collaborative filtering is user recommended titles he/she has not
seen but others with similar profile has and/or rated higher than average
(evaluation system is usually part of such portals)
• Profile is understood as passed title selection, but also other user attributes
such as social-demographics and other behavioural data
• In case of content-based recommendation predictive model is created for
each double <user/title> predicting probability of liking based on user
attributes and title parametres (passport – categorisation, text description
etc.)
• The model is „trained“ and parametrized based on historical data
• Both methods have their pros and cons – it will strongly depend on specific
data domen which one will function better – both methods can be also
combined
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11. 2. Recommendation algorithm – methodics
• We suggest to apply both approaches on historical data using some of the
machine learning validation techniques, ie user choices already known will
be predicted measuring acuuracy of the algorithm applied
• In case of collaborative filtering we will use method of nearest neighbor
classification selecting pre-defined number of users with highest afinity to
clasified user
• For transfering text anotation of titles to attribute representation (necesary
for nearest neighbor classification) we will use bag of words method known
from information retrieval practise
• In case of content-based recommendation we will use standard predictive
machine learning algorithms again with transfering text content to atributes
with use of bag of words
• If any data on interrelation between titles (same actors, directors, genre)
and/or users (family) we will use relation attributes technique
• Best of bread approach will be then applied to final solution
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12. 2. Recommendation algorithm – valuation
• Pros
– Comprehensive, universal and
„self-learning“ behavioural models
– Relatively easy to implement
– Reasonable pricing model
– Good results – especially over
longer time
• Cons
– Need for learing – the algorithm
needs some time to optimise its
perfomance – afinity of
recommendations to user profile
– Longer implementation time
– More complicated development
and project risks
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 12

13. 3. Universal recommendation engine - architecture
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 13
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
User profile:
•Registration data
•Film database
•Actor database
•Browsed pages
•…
•Other sites
B2C
CRM
B2C
CRMcustomer DBcustomer DB
Universal rec engineUniversal rec engine
Tailor made recommendation algorithm with self-
learning capabilities based on customer DB and fully
integrated into Voyo user interface will suggest
personalised content to users.
Recommendation algorithm may be used for customer
profiling for the use of CRM system for the consequent
segmentation, campaigns and service model.

14. 3. Universal recommendation engine – methodics
• CME send notification about each particular action/request which the user
did on your site via a private API to the SynopsiTV – movie watched, if is it
finished, liked (rating system), etc. Real time (when the user performs a
particular action), or agregated in batches
• SynopsiTV generates and mix together two types of recommendations:
– Personalized recommendations (high priority)
• Based on the current preference of the user, recommendations are primarily based
mainly on what in recent weeks the user watched and what he likes and dislikes
• All recommendations are based solely on the user, his taste and his viewing history.
The recommendations are generated in real time and they are accessible in very few
seconds. They are broken into genres or categories by your choice (runtime, movie
studio, color/black, MPAA rating, etc.)
• Personalized search: For each of your users are generated different search results
based on their preferences.
– Bob will search for "harry" and his first result will be "Harry Brown", because he likes a lot of dramas.
– Marie, which will search for "harry" too, will see as the first result "Harry Potter", because she likes it
and she realy loves the fantasy genre.
• Personalized notification (e.g. emails): If the user is inactive for a certain time, our
system generates for him new personalized recommendations which could be send to
his email address (we are not asking you for the user email, you will be sending the
email, we are just generating the content)
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 14

15. 3. Universal recommendation engine – methodics
• SynopsiTV generates and mix together two types of recommendations:
– Social recommendations (low priority)
• System tries to estimate films and serials that do not fall into the current user's profile,
but he could enjoy them in the near future. These recommendations are intended to
disturb a "bubble" and allow a user to discover new genres and new movies/tv shows
• Recommendations: If your system supports social graph (e.g. following, friending, etc.),
the recommendations could be slightly affected by movies watched by user's friends. In
this case, the recommendations are available in real time for all affected users.
• Finding more friends: Based on the similar taste or a social graph of the user, our
system can recommend another users who might be interesting for him
• Social watching: Our system can recommend not only a movie or a tv show, but with
whom to watch them (who will like them as much as you will).
• Gamification: The system can reward users with virtual points that can be represented
by anything from real prices, badges (you are a master commander, because you have
seen this year 100 films, etc.), or any other form of your choice
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16. 3. Universal recommendation engine – methodics
• Statistics
– Accurate statistics: system generates accurate statistics, whether global for all
users, segments, or individual users. It will allow you to more precise target your
marketing campaing to support growing segments or help the declining ones.
– New titles success prediction: Thanks to large amount of internal data our system
can estimates how many users would watch the movie and if they will dis/like it.
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17. 3. Universal recommendation engine – methodics
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 17

18. 3. Universal rec engine – team, references
• Invite has invited its partner SynopsiTV which
has proven good results in specific film content
• The benefits of recommended solutions are
– recommendations are not based on an
anonymous group of users or on the similarity of
the users
– recommendations are generated in real time
– algorithms are time sensitive i.e. they reacts for
user's taste evolvement
– system changes with each user whom he tries to
understand
– wide choice of social tools (personalized search,
gamification, etc.) which can make the watching
experience better
– pay per use model
27.11.2016 Introduction, © Invite CRM, Ltd., 02/2011 18

19. 3. Universal rec engine – alternative options
• We have identified at minimum 6 free ware recommendation engines which
may be applied to CME needs
• Once universal engine version is selected we may have a closer look at
their performance within feasibility study
• Longer start up phase is required to test best option – significantly lower
costs may be the benefit
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20. 3. Universal recommendation engine – valuation
• Pros
– Comprehensive, universal and
„self-learning“ behavioural models
– Relatively easy to implement
– Best results
• Cons
– Higher price
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