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ghh2001

Gracie Han

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Movie Recommend via SVD IBCF UBCF
Research Question:How can I recommend the top 10 movies to certain people (users), based on their history of preference, or on the experience of similar people like them, or some other mechanism?Q Content based filtering, User based filtering, and SVD.
MovieLenseCollaborativeFilteringvsSVD
Content based filtering, User based filtering, and SVD are performed on the movielense data within the recommenderlab package. Their performance is also compared.
Jokes Recomders
This project compares different recommendation systems for certain users, on the jokes they might like. The comparisons are based on users (UBCF) and items (IBCF). Different normalization mechanisms are also compared.
MarketBasketAnalysis 2
Discussion Market Basket RS
Singular Value Decomposition
This is the singular value decomposition methods for the recommendation system, using movie lense dataset
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Khansari1
HM1 Ferrari
OralProjectFinal
Oral
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606 Lab7
Assignment 6 606
Lab6 DATA606
606 Hmwk5
606 Lab5
Week9 API
606 Lab4b Hui Han
Lab4a Hui Gracie Han
Assignment 3 606
Lab3-606
DB Creation SQLite fm CSV-Version2
Proj3_607
Technical Skills ONET
Tech Skills ONET Occupation
Week7 607
Read Json, HTML, XML data into R
Assignment3 AirlineData
Project1 607
Tournament Data
Week3 606 Exercise
HomeWork Chap1 - IntroStat
Hui (Gracie) Han Solution
LAB0 DATA606 Part2
606 Lab0 Part1
Week2 Solution
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