The document discusses implementations for scalable machine learning algorithms like collaborative filtering, clustering, classification, and dimensionality reduction. It provides examples of calculating similarity between users based on common movies rated and comparing different similarity metrics. It also discusses challenges like sparsity in rating datasets and techniques for improving performance like dimensionality reduction.

1. - S.V.Giri

2. Provides implementation for Scalable Machine Learning Algorithms
-- Wikipedia
Machine Learning Algorithms
 Collaborative Filtering
 Clustering
 Classification
 Dimensionality reduction
 Anomaly detection
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3. Similarity – Number of Common Movies between users
SIM(US1, US2)= 0 , SIM(US1, US3)= 3
Threshold for Similarity
The more the user watches movies, the more is he similar to others
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4. Cosine Similarity
Tanimoto Coefficient
Pearson Correlation Coefficient
Euclidean Distance
LogLikelihood Similarity
Spearman Rank Correlation
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5.  A measure of similarity between 2 vectors
 Values from 0 to 1
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i ii
yx
yx
yx
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),cos( 
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6. Cos(US1,US2)= 5*0 + 4*2 + 0*4 + 1*5 / (8.19*6.71) = 0.22
Cos(US1,US3)= 5*5 + 4*5 + 5*5+ 0*2 + 1*1 / (8.19*8.94) = 0.97
Cos(US2,US4)= 0*1+4*4+5*3/(6.71*5.09)= 0.91
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7. October, 2006 – 1 million Dollar
Training Data Set
Users – 480,000
Movies – 18,000
Pairs – 100 Million
Ratings : 1- 5
Test Data Set
Ratings to be predicted – 1.5 Million Pairs
Metrics - RMSE
Cinematch – 0.9514
Best RMSE – 0.8563 (Cracked by – BelKor’s Pragmatic Chaos)
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8. Actual Values –
(us1,mv1,5)(u2,mv1,3)(u3,mv2,1)(u5,mv6,3)
Predicted Values –
(us1,mv1,4)(u2,mv1,3)(u3,mv2,2)(u5,mv6,2)
RMSE = √((5-4)²+(3-3)²+(1-2)²+(3-2)²)/4
= 0.86
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9. (US4, SW4) =??
Average of all the other user ratings for this movie
= 4+2+5/3 = round(3.66) = 4
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10. 10

11. Sim(US4,US1) = 0.19
Sim(US4,US2) = 0.91
Sim(US4,US3)= 0.35
US4 is similar to US2
Hence Rating(US4,SW2)= Rating(US2,SW2)=2
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12. Sim(US5,US2) = 0.955
Rating(US5,SW2)= Rating(US2,SW2)= 2
Avg(US2)= 3, AVG (US5)=2
Rating(US5,SW2)= Rating(US2,SW2)+ AVG (US5)- AVG(US2)= 1
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13. Training Data Set
Users – 480,000
Movies – 18,000
Ratings – 100 Million
Sparse Matrix
Actual Possible pairings – 480,000*18,000 = 8.6 Billion
Pairs Present = 1.1%
Best Representation:
(Key, Value) pair
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14. Similarity Matrix Computation
Time Complexity
User based Similarity :
For all Users (Sim (UserVector, User vector))
Number of users = 480,000
Number of user pairs = 480,000 * 480,000= 230 Billion user pairs
Number of comparisons for one sim val = 18000
Total Computations = 230 Billion * 18000 = 4140 Trillion
Operations
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15. Dimensionality Reductions :
SVD (Singular Valued Decomposition)
MinHasing
Locality Sensitive Hashing (LSH)
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16. US1 SW1 5
US1 SW2 4
US1 LOTR1 5
US1 Notting Hill 0
US1 Mean Girls 1
US2 SW1 0
US2 SW2 2
US2 LOTR1 -
…
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17. 17

18. User Based – Similarity Between Users
Product Based – Similarity Between Products
Click Based – Based on user Clicks/Likes
Content Based – Based on tags, reviews, ratings.
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20. Cos(SW1,SW2)= 0.94
Cos(SW1, Notting Hill)= 0.233
Cos(Mean Girls, Notting Hill)= 0.94
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US1 US2 US3 US4
SW1 5 0 5 1
SW2 4 2 5 -
LOTR1 5 - 5 -
Notting Hill 0 4 2 4
Mean Girls 1 5 1 3

21. The Firm ∼ The RainMaker
The Bourne Identity ∼ The Bourne Ultimatum
 Uniform Weight
 Weighted Parameters
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Author Category Year
The Firm John Grisham Thriller 1991
The Bourne
Identity
Robert Ludlum Thriller 1980
The Bourne
Ultimatum
Robert Ludlum Thriller 1990
The Rainmaker John Grisham Thriller 1995

22. Problem:
 User Reads a news article
 Find Similar news articles
 Don’t find same news article.
How to convert document into a vector?
 Extract all the words
 Remove stop words
 Identify Named Entities
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23. New Movie
- No views (or less views)
- No similar Movies
New User
- No ratings (fewer ratings)
- No similar Users
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24. Thank you
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