1. A Novel Recommender Algorithm for Bipartite
Networks employing Association Rules to Discover
Characteristics Intrinsic to the Content
A.J.M. Korale & N.S.Weeraman
Abstract: Faced with an extremely competitive landscape and low margins, Mobile
Telecommunications Operators are motivated to seek new sources of revenue streams by promoting
Value Added Services (VAS) to their customers. This strategy in effect exploits the novelty and
attractive power of the supplementary service portfolio to draw in new subscribers and retain and
satisfy the existing base. However, delivering satisfying content to a vast and diverse customer base
with widely differing tastes and expectations is a challenge faced by Mobile Telecommunications
Operators. Thus Intelligent Algorithms in the form of Recommender Systems are employed to match
available content with customer tastes and desires. Such algorithms usually require considerable Meta
data on the content and detailed customer information and their consumption patterns to attempt this
match. As the Meta data does not and indeed cannot always accurately characterize the intrinsic
properties of the content, which is the basis of its attraction to a consumer, the match is at best not a
very accurate one.
In light of this we propose an enhancement that operates without recourse to the Meta data of the
content but instead employ the novel idea that certain items of the content itself can be used to
represent the larger set. This result is established through a process of Association Rule discovery
where it is shown that Ring Back Tone Songs (RBTS) that group together do in fact exhibit similarities
at an intrinsic and fundamental level rather than that which would be observed by merely comparing
the Meta data of the content.
We also show that due to this grouping of songs via the estimation of associations we are able to
broadly categorize the available song choices using selected songs as representative categories. It then
allows us to quantify the strength of the relationships between songs by using the rules so arrived at
through performance measures derived from confidence and lift.
Thus the algorithm provides a more accurate match between a customer and his preference for RBTS
content by analysing song content at a fundamental and intrinsic level and recommends song content
using the properties inherent in the songs. It also improves its match by utilizing the similarities
between customers who prefer a certain category of song by analysing the single mode representation
of the customer vs. content Bipartite graph.
Keywords: Recommender Systems, Collaborative Filtering, Content Filtering, Bi-Partite Network
1. Introduction
We live in an era in which mass marketing is
increasingly replaced by mass customisation,
where catering to the individual needs of a
consumer is the desired strategy as opposed to
marketing to large groups of customers. The
thinking has thus shifted from meeting the
requirements of groups of customers or larger
segments with similar characteristics to
satisfying the needs of individuals, or a
segment of size one. This fundamental change
in approach to the customer is evidenced by the
ability to customise a new PC or even an
automobile by selecting combinations of
desired features online before the order is
placed for a fully customised product.
The vast array of choice facing a customer
however makes it practically impossible for a
consumer to make a detailed assessment of
each individual option before making a choice,

2. Eng. (Dr.) A. J.M. Korale, C. Eng., MIE(SL), B.Sc. Eng.
(Wisconsin), M.Sc. Eng. (Columbia), Ph.D. Eng. (Imperial
College), MBA. (Sri Jayewardenepura), Consultant - Data
Scientist, Millennium Information Technology.
Eng. (Mr.) N. S. Weeraman, AMIE(SL), B.Sc. Eng.
(Moratuwa), MBA. (Sri Jayewardenepura), Unit Manager,
Business Intelligence, Dialog Axiata PLC.
necessitating the use of Recommender Systems.
Thus it is in our everyday experience when
booking a hotel, making an airline reservation
or buying a book on Amazon that we are
guided by intelligent agents in making a
suitable choice.
Mobile Telecommunications Operators (MTO),
in addition to their core business of providing
Mobile Wireless Telecommunications Services
also offer Fixed Wireless Services, Broad Band
Internet, and Direct to Home Broadcast
Television Services, and so have a wide variety
in their product offering. While making the
choice between different product types as
above may be more clear cut for a particular
customer, when faced with the task of selecting
a particular talk time package for the Mobile
Telecommunications Service or the selection of
a particular set of TV channels via a channel
package however, the buying decision is more
complex. Thus, so it is with respect to value
added service (VAS) offerings at Mobile
Telecommunications Operators where there can
be thousands of applications catering to
different tastes and interests. It is particularly
so in the case of the RBTS offering where there
are nearly 10,000 songs to choose from and
nearly one million subscribers to the service.
The main challenge for a Mobile Operator is
then matching this vast array of choice with its
customer base so that the most suitable content
can be directed to the appropriate customer in a
way that tastes and expectations are satisfied.
As it is most often the case that the customers
themselves are unaware of the vast choice in
the service offering, it is up to the Operator to
make the match between a customer and a
product and notify the options or available
choice to the subscriber. In the case of Mobile
Telecommunications Operators this task is
somewhat easier as there is a direct Digital
Marketing Channel to each individual
subscriber through the Short Message Service
(SMS) and other automated means. Thus the
Operators can target each individual subscriber
via "SMS offers" carefully designed to match
the individual requirements of each customer.
2. Current State of the Art in
Recommender Systems
The current state of the art with respect to
Recommender System Algorithms to our
knowledge is broadly based on two concepts;
the ability to discover groups of users who like
products similar to the ones in the portfolio of
the target customer and then suggesting those
products that the customer group have liked
but has not yet been experienced by the target
customer (Collaborative Filtering), and the
ability to find groups of products similar to the
products liked by the target consumer and
recommending those products that have not yet
been experienced by that particular consumer
(Content Filtering) [2]. These algorithms are
then based on making “similarity”
determinations with respect to "similar
customers” or "similar products".
More advanced algorithms factor in the
demographics, geography, Income and other
related characteristics of the subscriber in
addition to the consumption behaviour to
compare between customers and arrive at
better groups of "similar" customers [1].
The vast majority of recommender algorithms
also utilise detailed Meta data on the content
derived from databases and from the content
creator and distributor to characterize each item
of the content and then utilize this information
to find other similar items. This characterization
usually takes the form of classifying the content
in to genre's, performing artists, creative
personnel involved in the direction and
production of the content and production
houses involved, in an effort to finely and
accurately describe as much of its outwardly
visible and easily definable properties [3].
However this type of classification cannot
capture all of the intrinsic qualities inherent in
the content which is more about the feelings it
inspires, images that come to mind and
emotions it stirs. It is only a customer who has
experienced the content that can if at all
describe in what ways a particular piece of
content is appealing and this knowledge is
usually absent in the standard current
techniques used in Recommenders.
In spite of this, a variety of approaches ranging
from correlation based techniques to machine
learning algorithms using decision trees and
neural networks are successfully used in the
these Recommenders.

3. Other approaches that consider association
rules in recommenders do not in general use
rules to categorise items of content and use
those single items to represent larger groups as
we do in our algorithm [3]. In particular,
association rule based recommender algorithms
that use clustering to identify user groups does
so by utilizing other criteria which then may
not result in grouping the content on its
intrinsic properties [5]. This method also does
not consider the properties of the consumer vs.
consumer network neighbourhood in arriving
at its recommendations.
The proposed association rule generation
algorithm to the best of our knowledge is a
novel scheme that is based on existing
principals. It is designed to efficiently generate
rules utilising components of rules from
previous stages and so uses items that are
already known to have fulfilled constraints on
Confidence and Lift.
The proposed scheme is also able to consider
several layers/levels of neighbours around a
target consumer and this approach combined
with an association rule recommender we
believe is a new configuration for this type of
algorithm.
3. The underlying basis for a Novel
Association Rule based
Recommender
It is usually very difficult to analyse the
underlying characteristics of a product chosen
by a consumer and examine what makes a
particular consumer prefer such a product
merely from the basket of goods chosen by that
consumer or by observing a group of
consumers similar in characteristics to the
customer under study as there usually is no
prior "meta data" of the product or content
readily available. In the case of movie or music
recommender systems for instance, it would be
possible to analyse each film or song by the
performing artists, genre, production houses
and a host of other ancillary information that
could potentially be gathered by a content
provider and used as input to a recommender
algorithm.
In the case of an RBTS Recommender however,
all of the product items are of one type (songs)
and thus we need only find associations and
relationships between songs. While there is
considerable Meta data that is associated with
song content, for the purposes of this paper and
the algorithm described herein, we did not
have access to such attributes. In light of this
we propose an algorithm that operates in an
environment where we do not have ready
access to the Meta data of the content, but
instead employ the novel idea that the content
itself can be used in the characterization of
itself. Thus for the particular case of RBTS
subscriptions by customers of Mobile Telecom
Services, we are able to categorize the RBTS
(music choices) by using a set of song choices.
In other words we categorize and reduce the
number of potential song choices using a subset
of the songs to represent certain groups of
songs.
Thus it is our contention that certain types of
content can be characterized by the properties
inherent in the content itself, particularly when
the content is used for a very personal and
personalized purpose. In our use case, we
contend that the Ring Back Tone Song (RBTS)
chosen by each customer to indicate a ringing
telephone to a caller is one that his highly
personal to the customer as it is chosen with a
view of representing the customer to the
outside world and hence from the point of view
of that customer it is also one that has a certain
appeal and great deal of intrinsic meaning.
In this paper we also study how multiple songs
are selected by a particular customer and
establish that songs that "go together" are songs
that are similar to one another. This similarity
goes beyond the usual commonalties that can
be expected between songs that are "similar" to
one another in the sense of being sung by the
same artist, or belonging to the same genre, or
those using a particular type of instrumentation
for example. Instead we show that the songs
that are found to be "similar “are so on a much
deeper and intrinsic level; like being similar in
theme, meaning, and in concept. We believe
that this result is due to the inherently personal
nature of the choice of a Ring Back Tone Song
that may also be used to gain deeper insights in
to the very personal and personalized
preferences of the customer making the choice.
We establish these results through a process of
Association Rule discovery where it is shown
that songs that group together in fact exhibit
similarities at an intrinsic and fundamental
level rather than that which would be observed
by merely comparing the Meta data of the
content. In fact there is no guarantee that items
of content that exhibit broad similarities with
respect their Meta data would appear similar or
more importantly that it would have similar

4. appeal to a particular customer. This is the
result of the wide variety in the content that is
available, particularly in music that may share
the same type of Meta data and other ancillary
information but differ in its intrinsic qualities
that appeal to a customer.
We also show that due to this grouping of
songs via the estimation of associations we are
able to broadly categorize the song choices
using the songs themselves as representative
categories. It then allows us to quantify the
strength of the relationships between songs by
using the rules so arrived at and by
performance measures such as confidence and
lift.
We then take the analysis a level deeper by
considering the properties of the customer to
customer network that lies at the heart of the
customer vs. song bipartite graph by translating
it in to its corresponding customer vs. customer
single mode representation.
In this way we build a recommender algorithm
that has the ability to analyse song content at a
fundamental and intrinsic level and suggest
song content using the properties inherent in
the songs itself while also utilising the
similarities between customers who prefer a
certain category of song.
4. Psychological Factors Underlying
the Proposed Recommender
Algorithm for RBTS
We believe that that Ring Back Tone Songs are
an aspect of oneself a subscriber wishes to
project to the outside world and is thus an
insight in to the subjects "personality". We also
believe that the RBTS choice is a deeply
personal choice and is therefore reflection of
some innate quality or characteristic of a
subscriber's personality.
We can show from the results of the association
rule analysis and broad song choices made by
large groups of the subscribers that when more
than one song is selected by a subscriber, the
songs within that group show similarities at an
intrinsic and fundamental level. We believe this
may be reflection or an insight in to some
aspect of the subscriber’s personality. As
"personality" is more or less fixed upon
reaching maturity we believe that the intrinsic
quality of a person’s choice of RBTS is also
unlikely to change much.
In this paper we are not concerned with the
underlying factors that were responsible for the
formation of a particular personality type but
only use the insight that it is unlikely to change
much over time and that it will also govern or
influence personal choices such as selecting a
RBTS.
While there will be exceptions to this line of
reasoning the results of the association rule
analysis do show that songs that show strong
associations with one another are "similar". As
the association rules are also a reflection of the
aggregate choices of a large number of
subscribers, our original line of reasoning
seems to be justified.
5. Main Contributions of the Paper
In this paper we employ the novel idea that
certain items of content can be used to represent
the larger set to which it belongs. We show that
certain RBTS songs selected on the basis of their
strong associations to other songs can be used
as representative categories of the larger song
set.
We also show that the proposed recommender
is able to match consumers taste for content
without recourse to its Meta data as it operates
by discovering and matching the underlying
intrinsic properties of the content such as its
theme, meaning and concept which is the basis
of its attraction to a consumer.
This is facilitated in part by a novel Association
Rule mining algorithm which efficiently
generates rules at each stage by merging the
components (antecedent and consequent terms)
of the rules in the prior stage to form the
antecedent of the subsequent stage. In this way
we generate only the minimum set of rules that
satisfy certain a priori constraints on confidence
and lift.
We improve the match between the
recommended song set and consumer’s own
taste in song choice by considering the song
choices of consumers most like the target
consumer. This is accomplished by considering
the network properties of the consumer’s
immediate neighbours identified from the
consumer vs. song bi-partite network.
We also show that a better match across a wider
variety of song content can be achieved by
combining the results of the suggestions
obtained by considering a consumer’s own
choice of songs with the suggestions obtained

5. by considering the song choices of his
neighbours that are determined by placing
constraints on the proximity of such individuals
to the target consumer in the consumer vs.
consumer network.
5.1 A novel Association Rule Mining
Algorithm for the purposes of Recommending
RBTS
A novel Association Rule Mining algorithm
that generates all the unique combinations of
the items that comprise the antecedent and
consequent terms of a rule while satisfying
apriori constraints on support for that rule. The
algorithm is formulated in such a way as to
utilize the rules generated in the (N-1)th stage
to form the rules at the Nth stage by selecting
only those rules that meet the minimum
support requirement in the (N-1)th stage. This
is done by forming the antecedent of the rule in
the Nth stage by merging the antecedent and
consequent terms of the rule from the (N-1)th
stage which we know meet the constraints on
support and then computing the support of this
new rule.
In this way we avoid having to generate apriori
all possible combinations of items that statisfy
the minimum support criteria and then test the
support of each newly formed rule. In our
scheme we only need to test rules from the
prior stage that we know have met the
minimum support criteria. Through this
method we are also able to automatically
generate all rules progressively proceeding
down each stage until all rules with all possible
unique combinations of items in the antecedent
and consequent parts have been created.
It is also possible to filter the rules apriori as
they are being generated by enforcing
conditions on confidence and lift if it is so
desired. Thus there is a gain in efficiency
obtained by only utilizing those rules from the
previous stage that meet certain performance
criteria to create the rules for the subsequent
stage by merging the antecedent and
consequent parts of rules that meet the minim
support criteria from the previous stage to form
the antecedent part of the rule at the next stage.
5.2 The use of Association Rules to Categorize
and Classify Ring Back Tone Songs using
Characteristics Intrinsic to the Content.
We establish that in the case of Ring Back Tone
Songs, the songs that are show strong
association with each other are largely those
that are similar in content and character
showing remarkable similarities in its deep and
intrinsic qualities. Through this result we are
able to classify and categorize the songs by
grouping the songs that are strongly associated
in to groups. This is also a key result that helps
in the recommendation of new song content to
a subscriber through song categories that are
taken to represent smaller groups of songs.
We establish this result without recourse to the
Meta data of the songs or any other content
related attributes using only the relationships
estimated from the Association rule mining.
5.3 Ranked Recommendation choices
estimated from Association Rules
The algorithm classifies RBTS according to its
intrinsic properties in to categories derived
from the items of content itself and
recommends items via the association rules
ranked according to confidence in a way that
accounts for the content choices of the
neighbours in the single mode projection of the
customer vs. item bipartite graph.
After the rules are mined as per (1) and
classified as (2) above, the RBTS subscription
portfolio of the subscriber is used to determine
which rules should apply in the
recommendation by selecting those that have
the same antecedent as the song subscriptions.
Next the selected rules are ranked by
confidence and the possible consequent items
determined arriving at a set of choices ordered
by the degree of "correlation" to the existing set.
The neighbourhood of the subscriber that is
found by placing limits on the number of songs
in common with other subscribers and size of
the subscription set of the neighbours in
relation to the customer in question is also used
to determine which of the ranked consequent
choices are actually recommended.
6. Association Rule Discovery
Algorithm for RBTS
The Association Rules are mined from an
Incidence Matrix (IM) by placing apriori limits
on the support for each Item which is the
column sum of IM. IM is formed as a customer
vs. Item matrix, by allocating unity for every
song that has been experienced by each
customer. If a Customer has selected the same
song more than once, the entry in the
corresponding song column in the row
allocated for that customer is still unity.
Once the column sum (IMsum_min) is formed
the particular columns that do not meet the

6. minimum support criteria (min_supp) are not
considered and eliminated from the analysis as
a rule that incorporates that item cannot in any
case meet the minimum support requirement.
The rule discovery then proceeds to the first
stage by determining all pairs of items that
meet the minimum support criteria. The
support for a pair of items is then simply the
dot product of the corresponding columns of
IM. In the next step the pairs of items that meet
the minimum support criteria are selected and
the support and confidence of the rule are also
determined.
In the second stage only the rules that met the
minimum support criteria from the first stage
are selected to form the rules. The antecedent in
the second stage is then formed by merging
antecedent and consequent terms from the
rules (that met the minimum support criteria)
of the first stage. The three item rules (2 in
items in the antecedent derived from stage 1,
and one new item in the consequent) that can
be formed that meet the minimum support
criteria can only come from the set of terms
selected from stage 1 and the set IMsum_min.
For example, let A, B, C, D be items that meet
the minim support criteria in IM.
In the first stage all pairs {A,B}, {A,C}, {A,D},
{B,C}, {B,D}, {C,D} are tested for minimum
support. (There will be 4C2 = 6 such terms to be
tested.)
A->B is a valid rule, only if the support for AUB
meets min_supp criteria
In the second stage A and B (and all other pairs
that met min_supp) (are merged to form the
antecedent of the rule at stage 2 if they meet the
min_supp criteria. (Let us assume that {A,B},
{A,C} and {C,D} meet the min_supp criteria)
Thus in stage 2
A&B->C is a valid rule only if the support for
AUBUC meets min_supp criteria (and similarly
we test support for rules A&B->D, A&C->B,
A&C->D and C&D->A and C&D->B)
The rule discovery proceeds in this manner
until all unique combinations of items have
been created in the rules.
6.1 Definitions
{Support (A->B)= N(AUB)/N (1)}
Where N(AUB) represents the number of times
items A and B are bought together in the whole
transaction set and N is the number of rows in
IM (corresponds to the number of unique
subscribers or transactions)
{Confidence(A->B)
= Support(AUB)/Support(A) (2)}
= P(EA&EB)/P(EA) (3)}
= P(EB/EA) (4)}
Where P(EX) is the probability of item X being
bought in a particular transaction
Confidence of a rule (A->B) is a measure of the
validity of the rule, it has a conditional
probability interpretation in that, it is the
probability that B occurs when A has already
occurred [4]. In market basket parlance it is the
probability of item B being bought given that A
has been bought in a particular transaction.
Loosely speaking, we use the confidence
measure in our reasoning as an indication of
the degree of correlation between songs when
we have rules of the form song1 -> song2. It is
also used to rank the association rules
particularly when one common antecedent
gives rise to many consequent items.
The Lift is a popular measure used to measure
how likely a rule is if the items are independent
[4].
{Lift(A->B)
= Confidence(A->B)/Support(B) (5)}
{Lift(A->B) = P(EA&EB)/P(EA)P(EB) (6)}
In other words it is the number times how
likely it is that A&B are bought together when
the probability of buying A and the probability
of buying B are independent. (Ie when the
buying decision of item A does not influence
the buying decision of B and vice versa)
In this modelling we generate all combinations
of stage 1 rules that satisfy the min_supp
criteria as we need to match the antecedents of
the rules with the subscription portfolio of a
subscriber in order to generate
recommendation options. We use low
minimum support levels to ensure that we
generate a large number of rules and handle the
filtering and ranking via the categorization of
songs and rule confidence measure.

7. 7. Recommender Algorithm &
Strategy
The Recommender Algorithm determines the
best RBTS suggestions for a particular
subscriber by considering the items currently
subscribed to by the target subscriber and the
subscription pattern of that subscriber’s nearest
neighbours. This neighbourhood can be
determined from the single mode
representation of the Bipartite Network that is
derived by multiplying the incidence matrix by
its transpose IM*IMT
.
The resulting subscriber vs. subscriber network
is similar in form to an Adjacency matrix from
which the neighbours can easily be found. In
essence the neighbours of the target subscriber
are those that have at least some of the same
items in common.
Constraints are set on the size of the
subscription set of each neighbour in relation to
the size of the subscription set of the target
subscriber so that they are approximately of the
same size. Additionally to qualify as a
neighbour the subscriber in question must have
at least a certain minimum number of items in
common with the target subscriber. These
conditions are placed to ensure that the
neighbour set is more or less “similar” to the
target subscriber.
7.1 Determining Nearest Neighbours
1. For each subscriber determine all of the
other subscribers NS = {ns1,ns2,…,nsi,...,nsT}
that share at least one song item in common
2. For each of the neighbours nsi
determine the size of their subscription set nsiT
3. For each of the neighbours nsi
determine the number of songs in common nsci
with the target subscriber Si
4. The neighbour set is then the set of
subscribers that meet an apriori limit on the
nsiT and nsci
5. The limits can be set on the basis of the
size of the subscription set of the target
subscriber Si
6. So we can say a neighbour of the target
subscriber is one who is within (+/-) 50% the
size of the subscription set of Si and who also
matches between (+/-) 20% of the songs in the
subscription set of Si
7. The neighbour item set is then the
union of the set of items (RBTS) found among
the subscribers who meet conditions in 6 above.
7.2 Generate Association Rules
The Association rules for the RBTS items can be
obtained using the algorithm described in
section 6.
In this instance we generate only the level 1
associations with one antecedent and one
consequent term. We use low support and
confidence thresholds so as to capture as many
rules as possible. This is done with the object of
matching the antecedents in the rules to find
consequent terms that have high confidence.
7.3 Hybrid Recommender Algorithm
The hybrid nature of the algorithm arises by
combining the suggestions obtained via the
association rules with those obtained by
considering the song choices of the consumer’s
immediate neighbours that are identified via
the single mode representation of the consumer
vs. song bi-partite network.
In this hybrid approach the final recommended
song set is determined as the intersection of the
song set suggested for the consumer by
considering the consumer’s own song choices
with the choices of his nearest neighbours.
Thus, we endeavour to find better matches for a
consumers taste in song content by using that
consumers own (prior) taste in songs together
with the tastes of consumers most like him.
This neighbourhood of similar consumers is
determined by their preferences for similar
types of content as the target consumer.
However there is sufficient variation across the
content preferences of the neighbours to ensure
that a diverse set of songs are available for
consideration. In essence we allow for a certain
degree of overlap in the tastes between the
target consumer and his neighbours to ensure
that there is sufficient variety in the set of songs
suggested by the neighbouring consumers
which is then observed in the final
recommendation.
1. For each subscriber obtain his
subscription song set S = {s1,s2,…,si,…,sN}
2. For each song si in the subscription set
S, obtain all rules Ri = {r1,r2,…,rj,…,rK} which
have same antecedent as si
3. From this rule set Ri, choose the rule
that has the highest confidence ri_max and the
corresponding consequent term Ci_max of the
rule.
It may be that in some cases more than one rule
from Ri may meet this criteria (if they have the
same max value for instance), in which case all
such consequent terms are extracted.

8. 4. The “available” song set {AS} is then
the set of consequent items which have the
highest confidence from each set of rules Ri
derived for each song si in the subscription set.
5. The recommended song set is then the
intersection between songs that are in the
available song set {AS} and the Neighbour song
set (set composed of the songs found in the
neighbours subscription set, across all of the
neighbours).
6. If the intersection is null or too small
the available song set is used as the
recommended set.
8. Results
We present some example results extracted to
demonstrate the match between the
subscription set of songs (Table 1) and the
corresponding recommended set of songs
(Table 2) for 10 subscribers drawn randomly. It
is evident that the recommendations do match
the subscriber’s song set to a great degree in
terms of the characteristics described
previously.
Table 1. Subscription Song Characteristics
Su
b
No
Subscription
Song Set of
Subscriber
Languag
e
Source Release
Theme
1
Theme
2
Theme
3
1 Raja Malige Sinhala Charts New Other
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
9
Ennodu Nee
Irundhaal Tamil Movie New Love
10
Dukama
Widala Sinhala Charts New Love Sorrow Losing
10 Hithumanapen Sinhala Charts New Love Sorrow Losing
10 Waradi Karala Sinhala Charts New Love Sorrow Losing
As discussed earlier a single song subscription
can give rise to multiple song recommendations
as each song appearing in the antecedent of the
rule be correlated with multiple consequent
songs that have the same maximum confidence
level.
In practice then the unique set of items taken
from all the recommended items for a
particular subscriber will be used (multiple
occurrences of the same consequent item in
Table 2 is left to demonstrate the working of the
algorithm)
If the recommended set is too small the option
of using the consequent items with the next
highest confidence level can be employed.
We also observed several cases where the
recommended matches were not in line with
our hypothesis. This resulted when certain
subscribers had songs that had broad appeal
across many tastes (like national anthem
themed songs).
Table 2. Recommended Song Characteristics
Sub
No
Recomm
endation
Song
Set of
Subscrib
er
Langu
age
Sourc
e
Relea
se
The
me1
The
me2
The
me3
1 Amma Sinhal
a
Kavi
Bana
New Moth
er
Moth
er
2 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
2 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
2 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
3 Awasana
Premayai
Sinhal
a
Charts New Love Sorr
ow
4
Hadawath
a Gahena
Sinhal
a
Charts New Love Sorr
ow
Losin
g
4 Awasana
Premayai
Sinhal
a
Charts New Love Sorr
ow
4 Awasana
Premayai
Sinhal
a
Charts New Love Sorr
ow
5 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
5 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
6
Kannukull
e
Tamil Movie Old Love
7 Awasana
Premayai
Sinhal
a
Charts New Love Sorr
ow
7 Dukama
Vidala
Sinhal
a
Charts New Love Sorr
ow
Losin
g

9. 8 Kanne
En
Kanmaniy
e.
Tamil Movie Old Love
8 Enna Oru
Enna Oru
Tamil Movie New Love
9
Idhayatha
i Yedho
Ondru
Tamil Movie New Love
9 Un Mele
Oru
Kannu
Tamil Movie New Love
10 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
10 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
10 Ayeth
Warak
Sinhal
a
Charts New Love Sorr
ow
Losin
g
In such cases there could be a large number of
subscribers with widely differing tastes who
also have this song in their subscription
portfolio giving rise to spurious associations
/correlations at high confidence levels. These
occurrences are in the minority and should not
noticeably impact the overall trend in the result.
We observed approximately 83% match
between the subscription set and the
recommendations.
8.1 Operationalizing the Recommender
A subscriber may download RBTS using
different channels such as IVR (Interactive
Voice Response), Web, SMS or copy from
another RBTS user. We favour the Out Bound
Dialler (OBD) as the main promotion channel
as the user can hear the recommended songs
before selection and also as it has been
successfully used to promote RBTS in the past.
Also a customized SMS sent to the target user
with recommended songs having a direct dial
short code can be used for users who are using
SMS and who are familiar with downloading
RBTS via direct dial method.
In particular we may target existing subscribers
who have many songs in their subscription and
who have a tendency to change their songs
fairly regularly. Although each subscriber is
different, the Operator has the ability to select
the best individual marketing channel to reach
each one by observing past RBTS downloading
patterns and core Telecommunication Service
usage behaviour of that individual.
9. Conclusion and Future Work
We established that the proposed algorithm
and model successfully matches a subscriber’s
taste for Ring Back Tone Song content by
recommending songs that are very much
similar in its intrinsic qualities such as type,
theme and content.
We also showed that the proposed Association
Rule discovery algorithm that derives rules for
the current level by evaluating rules that meet
certain performance criteria from the previous
level is sufficiently flexible and efficient in
order to generate the large number of rules at
low support constraints with the option of
filtering at apriori confidence levels.
Through our modelling we also established a
fundamental insight in to consumer behaviour
in this particular type of content by observing
that items of content preferred by consumers
are strongly associated / correlated when their
intrinsic properties are very similar. With this
insight we are able to successfully match the
tastes of the subscriber in recommended RBTS
content.
Although we employ only level one rules with
a single antecedent and consequent term in our
model the ability to generate the full set may
prove useful in subsequent models where we
use rule priorities and coverage concepts to
rationalize and derive only those most relevant
to the recommendation.
While the neighbourhood calculation did not
significantly contribute to improving the
recommendations in the current incarnation of
the algorithm, we envisage factoring in
subscriber profiling attributes such as
demographics, language preference and Mobile
Service usage criteria to improve on the ability
to find groups of songs from subscribers that
have more in common with each other.
10. References
1. Wilson, D., Symth, B., & Reily, J., “Case-Studies in
Association Rule Mining for Recommender
Systems”, Proceedings of the 2005 International
Conference on Artificial Intelligence, ICAI, 2005.
2. Lin, W., Alvarez, S., & Ruiz, C., “Collaborative
Recommendation via Association Rule Mining”,
Department of Computer Science, Worcester
Polytechnic Institute, International Workshop on
Web Mining, 2005.
3. Swathi, V., Reddy, S, “Music Recommendation
System Using Association Rules”, International

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4. Hastie, T., Tibshirani, R., “The Elements of
Statistical Learning”, 2nd
ed., Springer, USA, 2009,
488-499pp.
5. M. Sunitha Reddy, T. Adilakshmi, V. Swathi, A
Novel Association Rule Mining and Clustering
Based Hybrid Method for Music
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