[mlpack] Alternatives to neighborhood-based collaborative filtering for GSOC 2016

[Divyam Khandelwal]

Hello Ryan Sir,
After successful testing *Movilens* dataset 1 million ratings from 6000
users on 4000 movies data. I think the changes can be bring  by:
*1. Adding time dependency*
TimeSVD++
• Parameterize explicit user factor vectors by time
a u (t) = a u + α u dev(t) + א ut
• a u is a static baseline vector
• α u dev(t) is a static vector multiplied by the deviation from the user’s
average rating time
• Captures linear changes in time
• א ut is a vector learned for a specific point in time

*2.By Stacked Ridge Regression*
• Diminishing returns from optimizing a single algorithm
• Different models capture different aspects of the data
• Moral: Errors of different algorithms can cancel out
• Treat the prediction errors of one algorithm as input “preferences” of
second       algorithm
• Second algorithm can learn to predict and hence offset the errors of the
first
• improved accuracy

*3.KNN by User Optimized Weights*

I am trying to implementing cf algorithm by optimizing weights and time
dependency and will update you.
Am i going on right direction?
Can we make it more efficient by using stacked linear regression?
                                        Thanking You.


--
Divyam Khandelwal(VIT Pune)
Third Year B.Tech in Computer Science.
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