Recommendation Systems

Kai Yu

Recommendation Applications

2 Recommendation contributes 35% of sales in Amazon

Recommendation Applications

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Recommendation Applications

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Recommendation Applications

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Outline

§  Problem definition §  Pre-processing §  CF as classification §  CF as matrix factorization §  Extensions §  Challenges

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Collaborative filtering (CF)

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Formal definition

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CF as matrix completion

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Metric

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Outline

§  Problem definition §  Pre-processing §  CF as classification §  CF as matrix factorization §  Extensions §  Challenges

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Pre-processing: centering data

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Pre-processing: centering data

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Intro Prelim Class/Reg MF Extend Combo Conclude Centering Shrinkage

Centering Your Data

• What?• Remove bias term from each rating before applying CF

methods: r̃ui = rui � bui

• How?• Global mean rating

• bui =µ � 1|T |�

(u,i)⇤T rui

• Item’s mean rating• bui = bi � 1

|R(i)|�

u⇤R(i) rui

• R(i) is the set of users who rated item i• User’s mean rating

• bui = bu � 1|R(u)|

�i⇤R(u) rui

• R(u) is the set of items rated by user u• Item’s mean rating + user’s mean deviation from item mean

• bui = bi +1

|R(u)|�

i⇤R(u)(rui � bi)

Lester Mackey Collaborative Filtering

Shrinkage

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Intro Prelim Class/Reg MF Extend Combo Conclude Centering Shrinkage

Shrinkage

• What?• Interpolating between an estimate computed from data and

a fixed, predetermined value• Why?

• Common task in CF: Compute estimate (e.g. a meanrating) for each user/item

• Not all estimates are equally reliable• Some users have orders of magnitude more ratings than

others• Estimates based on fewer datapoints tend to be noisier

R =

A B C D E F User meanAlice 2 5 5 4 3 5 4Bob 2 ? ? ? ? ? 2Craig 3 3 4 3 ? 4 3.4

• Hard to trust mean based on one rating

Lester Mackey Collaborative Filtering

Shrinkage

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Intro Prelim Class/Reg MF Extend Combo Conclude Centering Shrinkage

Shrinkage

• What?• Interpolating between an estimate computed from data and

a fixed, predetermined value• How?

• e.g. Shrunk User Mean:

b̃u =�

�+ |R(u)| � µ+|R(u)|�+ |R(u)| � bu

• µ is the global mean, � controls degree of shrinkage• When user has many ratings, b̃u ⇤ user’s mean rating• When user has few ratings, b̃u ⇤ global mean rating

R =

A B C D E F User mean Shrunk meanAlice 2 5 5 4 3 5 4 3.94Bob 2 ? ? ? ? ? 2 2.79Craig 3 3 4 3 ? 4 3.4 3.43

Global mean µ = 3.58, � = 1

Lester Mackey Collaborative Filtering

CF as supervised learning

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CF as supervised leanring

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Naïve Bayes Classifier

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Naïve Bayes Classifier

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Naïve Bayes Classifier

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KNN-based CF

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KNN: how to measure similarity

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Intro Prelim Class/Reg MF Extend Combo Conclude Naive Bayes KNN

KNN: Computing Similarities

How to measure similarity between items?• Cosine similarity

S(⇤r.i ,⇤r.j) =⇤⇤r.i ,⇤r.j⌅������⇤r.i������������⇤r.j������

• Pearson correlation coefficient

S(⇤r.i ,⇤r.j) =⇤⇤r.i �mean(⇤r.i),⇤r.j �mean(⇤r.j)⌅������⇤r.i �mean(⇤r.i)

������������⇤r.j �mean(⇤r.j)

������

• Inverse Euclidean distance

S(⇤r.i ,⇤r.j) =1���

���⇤r.i �⇤r.j������

Problem: These measures assume complete vectorsSolution: Compute over subset of users rated by both itemsComplexity: O(

⇥Uu=1 |R(u)|2) time

Herlocker et al., 1999Lester Mackey Collaborative Filtering

KNN: choosing K

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Intro Prelim Class/Reg MF Extend Combo Conclude Naive Bayes KNN

KNN: Choosing K neighbors

How to choose K nearest neighbors?

• Select K items with largest similarity score to query item i

Problem: Not all items were rated by query user u

Solution: Choose K most similar items rated by u

Complexity: O(min(KM,M log M))

Herlocker et al., 1999

Lester Mackey Collaborative Filtering

KNN: forming weighted predictions

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KNN: User Optimized Weights

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KNN: User Optimized Weights

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KNN: User Optimized Weights

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KNN: globally optimized weights

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KNN: globally optimized weights

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Intro Prelim Class/Reg MF Extend Combo Conclude Naive Bayes KNN

KNN: Globally Optimized Weights

Benefits• Weights optimized for the task of rating prediction

• Not just borrowed from the neighborhood selection phase• Weights not constrained to sum to 1

• Important if all nearest neighbors are dissimilar• Weights derived simultaneously

• Accounts for correlations among neighbors• Outperforms KNN with similarity or equal weights

Drawbacks• Must solve global optimization problem at training time• Must store O(M2) weights in memory

Koren, 2008

Lester Mackey Collaborative Filtering

KNN Summary

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Outline

§  Problem definition §  Pre-processing §  CF as classification §  CF as matrix factorization §  Extensions §  Challenges

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Low-rank matrix factorization

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Low-rank matrix factorization

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Singular Value Decomposition

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SVD with missing data

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SVD with missing data

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Regularization

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Alternating algorithm

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Gradient descent algorithm

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Stochastic gradient

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Intro Prelim Class/Reg MF Extend Combo Conclude SVD Factor Analysis

SVD with Missing Values

Insight: Update parameter after each observed rating• ⇧auEui = ⇥au + bi(⌥au,bi� � rui)

• ⇧bi Eui = ⇥bi + au(⌥au,bi� � rui)

Stochastic gradient descent algorithm• Repeat until convergence:

1 For each (u, i) ⌅ T1 Calculate error: eui ⇤ (⌥au,bi� � rui)2 Update au ⇤ au � �(⇥au + bieui)3 Update bi ⇤ bi � �(⇥bi + aueui)

Complexity: O(UK + MK) space, O(NK) time per passthrough training set• No need to store completed ratings matrix• No K3 overhead from solving linear regressions

Takacs et al., 2008, Funk, 2006

Lester Mackey Collaborative Filtering

Constrained MF as clustering

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Low-rank MF: summary

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Outline

§  Problem definition §  Pre-processing §  CF as classification §  CF as matrix factorization §  Extensions §  Challenges

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Implicit feedback

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Intro Prelim Class/Reg MF Extend Combo Conclude Implicit Feedback Time Dependence

Incorporating Implicit Feedback

Implicit feedback• In addition to explicitly observed ratings, may have access

to binary information reflecting implicit user preferences• Is a movie in a user’s queue at Netflix?• Was this item purchased (but never rated)?

• Test set can be a source of implicit feedback• For each (u, i) in the test set, we know u rated i; we just

don’t know the rating.• Data is not “missing at random”• The fact that a user rated an item provides information

about the rating.• E.g. People who rated Lord of The Rings I and II tend to rate

LOTR III more highly.

• Can extend several of our algorithms to incorporate implicitfeedback as additional binary preferences

Lester Mackey Collaborative Filtering

Incorporating implicit feedback

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Time dependence

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Incorporate time dependence

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Combining methods

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Combining methods

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Combining methods

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Outline

§  Problem definition §  Pre-processing §  CF as classification §  CF as matrix factorization §  Extensions §  Challenges

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Challenges for CF

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Challenges for CF

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Challenges for CF

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