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Recommendation for dummy
1.
A Brief Introduction
to ! Recommendation ! (Fallacies & Understanding) Jeong, Buhwan (Ph.D)
2.
3.
4.
X Data-driven Automated Personalized
5.
Everything, but Nothing
6.
For anyone For one
in a group For a person For an item
7.
Explicit Rating vs
Implicit Feedback
8.
Content-based Filtering (CBF) Collaborative
Filtering (CF)
9.
Model-based CF Memory-based CF Matrix
Factorization (MF)
10.
User-orientation vs Item-orientation I Us Me I Is
11.
Similarity Measures ! Many common
items between users Many common users between items
12.
Similar Items? Similar Users? MxN Co-occurrence,
Set theory, Distance, Correlation, Cosine, Kernel
13.
Hybrid (Ensemble) Explicit Rating Collaborative
Filtering User Orientation Implicit Feedback + Content-based Filtering Item Orientation
14.
Search Recommendation Goal Retrieval Discovery Query Keyword User or Item Result Documents Items BM25 CBF PageRank CF Ranking Recency,
Quality, Filtering, Diversification
15.
ShoppingHow ! Item- & memory-based
CF with implicit feedback Hybrid with CBF using category, mall, brand info.
16.
Curse of Dimensionality
17.
n axa n axN MxN m = Mxa m
18.
MF = SVD
= LSA/LSI
19.
Let’s play music
20.
How to Evaluate?
21.
Accuracy vs User
Satisfaction
22.
Fast Iteration >>
Good Algorithm
23.
Post Analysis &
Review
24.
New Perspective ! Netflix’s micro
tagging/genre Amazon’s anticipatory shipping
25.
Cold-start Data sparsity Dimensional complexity Coverage Serendipity
& Diversity Explainability
26.
PR = P
+ M + R + F
27.
Just do it.
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