1. RECOMMENDER ALGORITHM FOR BIPARTITE
NETWORKS USING ASSOCIATION RULES TO DISCOVER
CHARACTERISTICS INTRINSIC TO THE CONTENT
By
Eng. (Dr) Asoka Korale, C.Eng., MIESL
Eng. Nilanka Weeraman, AMIESL

2. BUSINESS CASE FOR MOBILE RING BACK TONE RECOMMENDER SYSTEM
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• Intense Competition and Low Margins in Traditional Mobile Service Applications
 Require Automated System of Reference
 Promote High Value Ring Back Tone Content
 Customer mostly unaware of available choice
 Huge Opportunity - Vast Choice - 10,000 songs
• Exploit Novelty and Attractive power of Value Added Services (VAS) portfolio
• Wide variation in Customer Preferences and Tastes
But
 Match Customer tastes with available content
 Develop new Revenue Streams
• Wide variation in the “Type” &“Intrinsic” properties of content (Themes, Concepts, Genres…)

3. NOVEL CONTRIBUTIONS OF THE RECOMMENDER ALGORITHM
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• Characterize Content utilizing items of Content itself
• Consumer Taste and Preference matched via “intrinsic” properties of the content
• Fast Association Rule generation Algorithm satisfying a-priori constraints on performance measures
• Recommendation uses Individual’s “Taste” and preferences of “Nearest Neighbors in Taste”
 Neighbors from single mode form of Consumer Vs. Content Bi-Partite Network
 Rules at each stage generated via Rules from previous stage that met Performance criteria
A B
&
D&
CA B
& CA B
 Recommendation independent of “meta data”
 A song is itself a “category” of song – representing a larger set of songs

4. NOVEL ASSOCIATION RULE ALGORITHM
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Customer Vs. Song Choice Incidence Matrix
Song Subscription
Customer A B C D
Cx1 1 0 1 1
Cx2 0 0 1 1
.. .. .. .. ..
… .. … … …
Cxm
Total NA NB NC ND
Test support for all pairs 4C2 = 6 combinations
{A,B}, {A,C}, {A,D}, {B,C}, {B,D}, {C,D}
Stage 1
Let NA, NB, NC & ND exceed the minimum support criteria
Initial Stage – Test Support of all Individual Items
Stage 2
 Support for any combination of items (songs) can be
found by the “ sum of the element by element
product“ of the respective columns
STOP when a certain Rule depth “N” reached Or when all Rule
combinations are created subject to the minimum support criteria
Stage 3
Test Support for combinations with new consequent terms
taken from set that satisfy minimum support criteria
Test: A&B->C, A&B->D, A&C->B, A&C->D, C&D->A , C&D->B
Stage 2 Rules: A&B->C
Merge Antecedent and Consequent terms of Rules in Stage 1
& CA B
……………..
……………..
Merge Antecedent and Consequent terms of Rules in Stage 2
D&& CA B
Create Rules only for pairs that meet min support criteria
Assume {A,B}, {A,C}, {B,C}, {C,D} meet criteria
Stage 1 Rules: A->B, A->C, B->C, C->D
A B
 Example: Under the right conditions the
rules may take the form

5. RECOMMENDER ALGORITHM – NEAREST NEIGHBOR SET DETERMINATION
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Song Subscription
Customer A B C D E F G H
Cx1 1 0 1 1 0 1 0 1
Cx2 0 0 1 1 1 0 0 1
Cx3 1 0 0 0 0 0 1 0
Cx4 1 0 1 0 0 0 1 0
… .. … … … … … … …
1. Determine all “Cxi” that share at least one item in
common with Target Customer Cx1
2. size of subscribed song set of each Cxi = nsiT
3. For each Cxi determine number of songs in
common “nsci” with target customer Cx1
4. The neighbour set: is set that meet apriori limits
on “nsiT” and “nsci”
5. The limits can be set on the basis of the size of
the subscription set of the target subscriber Cx1
6. a neighbour of the target subscriber is one
• who is within (+/-) 50% the size of the
subscription set of Cx1 and
• who also matches between (+/-) 30% of the
songs in the subscription set of Cx1
7. The neighbour item set : union of the set of items
among the subscribers who meet conditions in 6
Customer vs. Song Incidence Matrix
Customer vs. Customer Single Mode Network
Cx1
Cx2
Cx3
Cx4
2
2
3
1
1
A,G
A,C,G
A,C,D,F,H
C,D,E,H
Target
Customer
Cx2, Cx3, Cx4 are “Nearest Neighbors” in “Taste”

6. Confidence(A->B) =
Support(AUB)/Support(A)
=P(EA&EB)/P(EA)
= P(EB/EA)
Where P(EX) is the probability of
item X being bought in a
particular transaction
Select all Rules R1 = {r1,r2,…,rj,…,rK} with
Subscription Song set {S} as Antecedent
r1: s1->sj
r2: s1->sk
r3: s2->si
r4: s3->sm (j,k,i.m intergers)
Filter to Find Rule(s) with highest Confidence
Rmax: Max( Confidence(ri) ) ∀i
Available Song Set {AS} = Consequent Songs of
Rule Set Rmax
Determine Neighbor Song Set
{NS}
Intersection of Neighbor and
Available Song sets
IS = {AS∩NS}
Recommended Set RS = {IS}
Recommended Set RS = {AS}
Target Customer’s (Cx1)
Subscription Song Set
S = {s1,s2,…,si,…,sN}
Support (A->B)= N(AUB)/N
If |IS| > Limit
Y
N
Target Customer Song subscription
A, C, D, F, H
Neighbors @
min weight = 2
A
C
D
E
G
H
RECOMMENDER ALGORITHM -
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Rules for Cx1 Confidence
A->B 0.8
A->D 0.5
A->C 0.6
C->B 0.5
C->H 0.6
D->E 0.4
F->G 0.5
H->K 0.4
Filtered Rules Confidence
A->B 0.8
C->H 0.6
D->E 0.4
F->G 0.5
H->K 0.4
Available Song Set
B
H
E
G
K
IS E, G, H
RS B, E, G, H, K
RS E, G, H

7. RESULTS – INSIGHTS INTO CONSUMER BEHAVIOR THROUGH CHOICE OF SONGS
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Emotions it stirs
Personality is more or less fixed in adults
Ring Back Tone Song Preferences based on Psychographic
considerations
Preference for Music Types and Personality are related
Song tastes “related to personality” would also be
more or less fixed
Ring Back Tone a deeply Personal Choice & an outward
Representation of the Consumer
Images that come to
mind
Feelings it inspires
Carries deeper meaning to consumer not
observed in “meta data”

8. ATTRIBUTES INFLUENCING SONG SELECTION
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Dimensions of Consumer Appeal
Source
Novelty Criteria
Release
Themes
Language
love sorrow family
Music Charts
Teledrama
Films
New Old
Sinhala English
Tamil Hindi
Instrumental
parents religion national
cricket
Events

9. RESULTS
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Sub No Subscription Song Set of Subscriber Language Source Release Theme1 Theme2 Theme3
2 Athi Mawa Rawatuwa Sinhala Charts New Love Sorrow Losing
2 Kandulu Hollala Sinhala Charts New Love Sorrow Losing
2 Sithama Ridawa Sinhala Charts New Love Sorrow Losing
3 Wen Weela Giyada Sinhala Charts New Love Sorrow Losing
4 Nidi Warapu As Walin Sinhala Charts New Love Sorrow Losing
4 Obath Giya Sinhala Charts New Love Sorrow Losing
5 Athi Mawa Rawatuwa Sinhala Charts New Love Sorrow Losing
5 Sithama Ridawa Sinhala Charts New Love Sorrow Losing
6 Ennai thalaatta Tamil Movie New Love
7 Dheere Dheere Hindi Movie New Love
7 Ananthen Aa Tharu Kumara Sinhala Teledrama New Love
8 Chinna Mani Kuyile Tamil Movie Old Love
Sub
No
Recommendation Song Set of
Subscriber
Language Source Release Theme1 Theme2 Theme3
2 Ayeth Warak Sinhala Charts New Love Sorrow Losing
2 Ayeth Warak Sinhala Charts New Love Sorrow Losing
2 Ayeth Warak Sinhala Charts New Love Sorrow Losing
3 Awasana Premayai Sinhala Charts New Love Sorrow
4 Hadawatha Gahena Sinhala Charts New Love Sorrow Losing
4 Awasana Premayai Sinhala Charts New Love Sorrow
5 Ayeth Warak Sinhala Charts New Love Sorrow Losing
5 Ayeth Warak Sinhala Charts New Love Sorrow Losing
6 Kannukulle Tamil Movie Old Love
7 Awasana Premayai Sinhala Charts New Love Sorrow
7 Dukama Vidala Sinhala Charts New Love Sorrow Losing
8 Kanne En Kanmaniye. Tamil Movie Old Love
8 Enna Oru Enna Oru Tamil Movie New Love

10. RESULTS – CASE OF SUBSCRIBER #3
Sinhala Charts New Love Sorrow Losing
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Video source: youtube.com
Video source: youtube.com

11. IMPLEMENTATION AND OPERATIONALIZATION
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Operationalized in Business Intelligence Systems at Dialog Axiata
Select the best marketing channel for each individual based on
 Download via Interactive Voice Response, Web, SMS or copy from another RBTS user
 customized SMS giving recommendations & direct dial short code for download
 past RBTS downloading patterns
 core Telecommunication Service usage behavior of individual
Options for Targeting existing subscribers or previous subscribers
subscribers with many song subscriptions
who change regularly
Non engaged users, who has
downloaded songs in past

12. CONCLUSION
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 Algorithm meets consumer taste in songs with new songs similar in its intrinsic qualities
 Fast Association Rule Generation with apriori performance constraints
 Recommendation via Tastes of the Individual and “Nearest Neighbor’s in Taste”
 Recommendations independent of Meta Data
 Content Characterization via Items of Content itself
• Consumers choices are correlated when the “intrinsic” properties of the content are also similar
 Intrinsic Properties - Type, Theme , Content…
• Novel Algorithm provides Individualized Recommendations matching Customer taste in Ring Back Tone Songs
• Retain and Satisfy the Existing base & Entice New Customers to Network
 Increase stickiness to Network

13. FUTURE APPLICATIONS OF THE ALGORITHM
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• Apply model to other products in portfolio with similar consumer appeal
• Insight in to identifying “Personality Type” of consumers
 Design campaign look and feel
 Push Products that meet personality criteria
 better targeting via Personality Type

14. THANK YOU
Q&A
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