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

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…)

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

Similar in Taste

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

NOVEL ASSOCIATION RULE ALGORITHM

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Customer Vs. Song Choice Incidence Matrix

Song SubscriptionCustom

er A B C DCx1 1 0 1 1Cx2 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 1Let 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 criteriaTest: A&B->C, A&B->D, A&C->B, A&C->D, C&D->A , C&D->BStage 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 criteriaAssume {A,B}, {A,C}, {B,C}, {C,D} meet criteriaStage 1 Rules: A->B, A->C, B->C, C->D

A B

Example: Under the right conditions the rules may take the form

RECOMMENDER ALGORITHM – NEAREST NEIGHBOR SET DETERMINATION

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Song SubscriptionCustomer A B C D E F G H

Cx1 1 0 1 1 0 1 0 1Cx2 0 0 1 1 1 0 0 1Cx3 1 0 0 0 0 0 1 0Cx4 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

31

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”

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->sjr2: s1->sk r3: s2->si

r4: s3->sm (j,k,i.m intergers)

Filter to Find Rule(s) with highest ConfidenceRmax: 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 = {ASNS}

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

ACDEGH

RECOMMENDER ALGORITHM -

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Rules for Cx1 ConfidenceA->B 0.8A->D 0.5A->C 0.6C->B 0.5 C->H 0.6D->E 0.4F->G 0.5H->K 0.4

Filtered Rules ConfidenceA->B 0.8C->H 0.6D->E 0.4F->G 0.5H->K 0.4

Available Song SetBHEGK

IS E, G, H

RSB, E, G, H,

K

RS E, G, H

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”

ATTRIBUTES INFLUENCING SONG SELECTION

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Dimensions of Consumer Appeal

Source

Novelty Criteria

Release

Themes Language

love sorrow family

Music Charts Teledrama

FilmsNew Old

Sinhala

EnglishTamil Hindi

Instrumental

parents religion nationalcricket

Events

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 Losing2 Kandulu Hollala Sinhala Charts New Love Sorrow Losing2 Sithama Ridawa Sinhala Charts New Love Sorrow Losing3 Wen Weela Giyada Sinhala Charts New Love Sorrow Losing4 Nidi Warapu As Walin Sinhala Charts New Love Sorrow Losing4 Obath Giya Sinhala Charts New Love Sorrow Losing5 Athi Mawa Rawatuwa Sinhala Charts New Love Sorrow Losing5 Sithama Ridawa Sinhala Charts New Love Sorrow Losing6 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 Losing2 Ayeth Warak Sinhala Charts New Love Sorrow Losing2 Ayeth Warak Sinhala Charts New Love Sorrow Losing3 Awasana Premayai Sinhala Charts New Love Sorrow4 Hadawatha Gahena Sinhala Charts New Love Sorrow Losing4 Awasana Premayai Sinhala Charts New Love Sorrow5 Ayeth Warak Sinhala Charts New Love Sorrow Losing5 Ayeth Warak Sinhala Charts New Love Sorrow Losing6 Kannukulle Tamil Movie Old Love7 Awasana Premayai Sinhala Charts New Love Sorrow7 Dukama Vidala Sinhala Charts New Love Sorrow Losing8 Kanne En Kanmaniye. Tamil Movie Old Love8 Enna Oru Enna Oru Tamil Movie New Love

RESULTS – CASE OF SUBSCRIBER #3

Sinhala Charts New Love Sorrow Losing

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Video source: youtube.com

Video source: youtube.com

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

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

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

THANK YOUQ&A

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