A Brief Introduction to !

Recommendation!

(Fallacies & Understanding)

Jeong, Buhwan (Ph.D)

Data-driven Automated

PersonalizedX

Everything, but Nothing

For anyone For one in a group For a person For an item

Explicit Rating vs Implicit Feedback

Content-based Filtering (CBF) Collaborative Filtering (CF)

Model-based CF Memory-based CF Matrix Factorization (MF)

User-orientation vs Item-orientation

Me I IsUsI

Similarity Measures !

Many common items between users

Many common users between items

Similar Items?

Sim

ilar U

sers

?

M x N

Co-occurrence, Set theory, Distance, Correlation, Cosine, Kernel

Hybrid (Ensemble)

Explicit Rating Implicit Feedback

Collaborative Filtering Content-based Filtering

User Orientation Item Orientation+

Search Recommendation

Goal Retrieval Discovery

Query Keyword User or Item

Result Documents Items

Ranking

BM25 CBF

PageRank CF

Recency, Quality, Filtering, Diversification

ShoppingHow !

Item- & memory-based CF with implicit feedback

Hybrid with CBF using category, mall, brand info.

Curse of Dimensionality

M x N M x a

a x aa x N

=

m

n

m

n

MF = SVD = LSA/LSI

Let’s play music

How to Evaluate?

Accuracy vs User Satisfaction

Fast Iteration >> Good Algorithm

Post Analysis & Review

New Perspective !

Netflix’s micro tagging/genre

Amazon’s anticipatory shipping

Cold-start Data sparsity

Dimensional complexity Coverage

Serendipity & Diversity Explainability

PR = P + M + R + F

Just do it.