Recommender Algorithm for a Mobile Alert Services Application

Dr. Asoka Korale C.Eng. MIET

Slide | 2

Necessity for “Automated System of Reference”

MOTIVATIONS FOR RECOMMENDER SYSTEMS Unlimited &

Complex Choices

“Mass Market” -> “Mass Customization”

“Individual” & “Specific” Preferences

Vital tool for E-Commerce success Pushing Products &

Services

Slide | 3

Content Based Graph Theoretic

Bayesian Classifiers

RECOMMENDER APPROACHES…….

Rule Based, Decision Trees, Neural Networks…….

Collaborative Filtering

Slide | 4

DECONSTRUCTING A “USER” – TWO MAIN COMPONENTS

User Preferences for Product Items – Variable part

Demographic Profile

Geographic Profile

Network profile

Consumer (VAS)

profile

Revenue profile

User Attributes derived from Mobile Telecom – Fixed part

Slide | 5

Transform Enhanced Ratings to reduce Dimensionality & ensure Meaningful correlation in “Concept“ space to identify neighbors

Use Customer Attributes to better correlate between other users because Ratings alone are too sparse

Independent User-User & Item-Item estimates for Ratings via users in neighborhood

IDEAS TO FORMULATE AN ALGORITHM Aim: Holistically match user Characteristics & Preferences with those of other users - to find groups of “Similar” users

Combine independent Rating estimates & Rank

Reduce Dimensionality by Clustering User’s Mobile Attributes & inputting cluster ID’s instead of attribute values to Enhance the

Rating matrix

Slide | 6

COMPONENTS OF PROPOSED ALGORITHM

Calculate Neighborhood of Similar Users

User-User Correlator - Predictor

Item-Item Correlator - Predictor

Combining Predictors & Ranking Ratings

User Attribute Clustering Singular

Value Decompo

sition

Slide | 7

The cost function that will be minimized toarrive at the clusters around the centroids

N

i

C

jji

mijm cxuJ

1

2

1

)(

][ ijuU 1. Initialize the membership function and centroids2. Update the membership function

C

k

m

ki

ji

ij

cx

cxu

1

12

1

3. Update the centroids

N

i

mij

N

ii

mij

j

u

xuc

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4. Check the convergence criteria, at kth iteration kk UU 1

jC

5. Stop if step 4. is satisfied, else return to step 2

RECOMMENDER ALGORITHM COMPONENTS - FUZZY C MEANS CLUSTERING

Slide | 8

RECOMMENDER ALGORITHM COMPONENTS – SINGULAR VALUE DECOMPOSITION

DDiagonolization: Where the columns of TD are the Ortho-Normal Eigen vectors of A and contains in the Eigen values in main diagonal

DD ATT '' 1

n

D

121 ATAT Tit follows that

IATAT T 111Pre and post multiplying

1ATU ATU 1setting rearranging

1TT T ATU T As T is Ortho-Normal Eigen vectors of ATA

in standard form

Slide | 9

Attribute 1 Attribute 2 ………. Rating 1 Rating 2 ………..

Ancillary Data ………. Ratings Data……….………. ……….. ………. ………… ………..

User Data:Mobile, Demographic,

Network, VASFuzzyClustering

UK

D VT

Singular Value Decomposition

SMS Alert

Product Items

Interest Category Items

Enhanced Ratings Matrix

Dimension ReductionSelecting Largest “k” Singular Values

U

Many to one Mappings

ALGORITHM: CLUSTERING, ENHANCED RATINGS & DECOMPOSITION

Slide | 10

ALGORITHM: NEIGHBORHOOD DETERMINATION, RANKING & PREDICTIONS

UK

jNj jr

jii r

rr

rrrpred

i

.User - UserCorrelation Predictor

Combine “User-User” & “Item-Item” Predictor Ratings

Item- ItemCorrelation Predictor

Rank: Combined Rating -> Next Best Product Recommendation

iNNeighborhood DeterminationUser - User Cosine Similarity

RESULTS

Slide | 11

SMS Alert Categories1. 'Business', 2. 'Mobile Apps‘3. 'Education'4. 'Entertainment‘5. 'Fun & Jokes‘6. 'Health‘7. 'Information‘8. 'News‘9. 'Other‘10. 'Social‘11. Sports‘12. 'Utilities‘13. 'sdp‘14. 'soltura'

Predict Majority of Next Product Recommendations accurately

Accuracy Rate 64%

0 5 10 15 20 25 30 350

20

40

60

80

100

120Normalized Cumulative Sum: Singular Values

Dimensions: Column No

Attribute Cluster Size

Singular Values

Slide | 12

Variable splitting increases sparsity but correlation more meaningful

Extremely sparse Ratings - very few product experiences per user

Optimal combination of User and Item Predictors

Improved Neighborhood calculation & Bi partite Graphs

CONCLUSION: CHALLENGES AND FUTURE DIRECTIONS

Poor Item – Item correlations due to Category Independence

Hybrid Predictive algorithms

FUTURE DIRECTIONS

CHALLENGES

THANK YOU