la-process-and-theory / recommender-systems Goto Github PK
View Code? Open in Web Editor NEWCreating a collaborative filter to recommend which unit to tackle next
Creating a collaborative filter to recommend which unit to tackle next
Hi Charles,
I've finished the HUDK4051 collaborative filter assignment but have some outstanding question about PCA we had to run in the end.
Is it appropriate that we apply absolute value (abs()) onto the eigenvectors to count the proportional contribution to PCs in all cases (what we did last semester)?
My previous training (hope not trapped in education stats again) taught me after we rotate the axes, the direction matters to interpret the PCs. So when looking at the loadings, I assume we should check each column and pick the positive values which make positive contribution (correlation), and hope that there is no overlap of such values in different PCs. If we apply abs(), we won't achieve this?
Also, I am assuming the purpose of running PCA on this is to see if "pred" unit will converge with other units, but what's the point to do doth interest & difficulty? Ideally, is it because we want to see "pred" unit will converge with other units NOT just for interest, but also for difficulty, too? Or simply put, what will be the worst case if student is just picking the easiest unit to their interest?
Thank you so much ~
The matching algorithm and making prediction for users' preference in this unit make me think about treating missing values. I talked to Charles and he suggest looking up "propensity score matching". It is not what we do in this unit but I think it is useful in various ways especially in an experiment. An useful tutorial here: https://www.youtube.com/watch?v=ACVyPp1Fy6Y
Some people were asking the HUDK4050ers about materials we used to get a grasp on basic R, I recommended the cheatsheet Charles shared with us and practice with it. It was really usefully to me. Hope this helps!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.