Personalized recommendation

1. Personalised
Recommendation - The
Mathematics
So that you don’t ask questions!

2. Introduction
Models
Prediction
Fitting - under, best & over
Training data

4. The Optimisation Problem
Minimising the objective function gives you
the prediction rule
● Least square
● Gradient descent

5. Flow
Model -> Training data -> Best fit (optimise) ->
Prediction Rule
where
(least square)

6. Recommender system
Predict the 'rating' or 'preference' that user
would give to an item
20% of sales in Amazon come from work of RS

7. Challenges
● Cold start
● Huge data
● Load on big data

8. Some simple solution
● Use interpersonal trust
● Use inferred trust circle
● News recommendation based on utilization
of real-time Twitter data
But,
Users with more friends tend to benefit more!

9. Proposed solution
● User personality/Personal interest -
experience level + category distribution
● Interpersonal interest similarity - interest
circle
● Interpersonal influence - inferred trust
circle (CircleCon Model)
● Natural item category tags - user specific
characteristics of rating dataset

10. Contribution
● What was there in last page
● A new approach by enforcing user’s latent
interest in model
● Experiments on extensive datasets to show
how this solves cold start

11. Problem statement
Given a user u in U and an item i in P for
which Ru,i is unknown, predict the rating for u
on item i using R, S, W and Q
where,
S -> Trust of u on v
W -> Interest similarity of u to v
Q -> Relevance of u’s interest to i

12. Basic Matrix Factorisation

13. CircleCon Model

14. ContextMF Model

15. PR Model

16. More stuff in the paper
● Model Training
● Experiments
● Performance Measures
● Evaluations and comparisons
● Lots of charts and numbers and equations
and things I don’t understand

17. Refernces
● http://www.quuxlabs.com/blog/2010/09/matrix-
factorization-a-simple-tutorial-and-implementation-in-
python/
● IEEE Paper - Personalized Recommendation Combining
User Interest and Social Circle

18. THANK YOU
No questions please :)