IET 2015 Recommender for Mobile Alert Services - linkedin
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Transcript of IET 2015 Recommender for Mobile Alert Services - linkedin
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
1
1
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