Mimic Collaborative fil-tering without user’s be-

havior data

이현성 , 윤규원 , Khawaji Faisal Mohammed

contents backgrounds

Collaborative Filtering Matrix Factorization

Model description Model structure Input

Background

Collaborative filtering From user’s behavior, calculate

similarity between users or be-tween items.

From that similarity, we can rec-ommend to user A

1. Items that User B who have high similarity with user A likes.

2. Items that is very similar with what user A likes.

Example (cosine similarity)

Similarity between items

Similarity between users

a user listens only a small fraction of songs.

One solution for this problem is to calculate similarity from factorized matrix, not from original song by user matrix.

Matrix factorization

dimensionality reduction. SVD, or other methods can be used to achieve this.

Music by User Matrix(each elements are how many times user j

listened music i)Music by # Feature matrix

Matrix M Factorized Matrix F

Matrix factorization

Matrix factorization itself has good features. It reduces noise

Calculation become simpler with lower number of columns

Matrix factorization reveals latent(hidden) factors from user’s behav-ior patterns.

Limitation of Matrix factorization(also collaborative filter-ing) It cannot recommend to newbies

It cannot recommend just released music

This is called the Cold Start Problem

Our goal : alleviate Cold Start Prob-lem Develop a model transform song into factorized vec-

tors without user behavior data.What this song’s row vector in F will be if it….

Music by # Feature matrix

New music

Assumption User preference to music is proportional to

Audio But not only audio!

Very similar song, but different pref-erences

User’s preferences are affected not only audio itself, but also other aspects of music.

such as who created it, popularity of the singer, released recently…

Model description

Merging(NN)

Output

CNN

MFCC features Other features available from tags

Neural network

Input MFCC data extracted from music

audio 그밖에 더 넣을 정보는… ?

Metadata( 넣을 까 말까 생각하는 것들… ) Tags

Bag of word representation Popularity of music/artist.

We think Youtube view count is good to capture music’s popularity.

MFCC

Bag of words representation for tags There are other models like word2vec or

kind of…

For tags, bag of words representation is good because tags have no context infor-mation (i.e. order of words is not given)

Example

Music A 신나는 여름음악 OST …

Music B 차분한 헤어졌을 때 … …

Tags 신나는 차분한 OST 여름음악 헤어졌을Music A 1 0 1 1 0

Music B 0 1 0 0 1

Thanks.Any questions?