Music Recommender Systems

Music Recommender Systems
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Who is Using Recommender Systems?

Recommender Systems
Summary：
http://en.wikipedia.org/wiki/Recommender_system
Keywords：
recommender systems、 association rules 、collaborative filtering、slope one、 SVD、KNN....

Algorithms
Association Rules
Slope one
SVD
….

Association Rules

Association Rules
Support：
Confidence:
Algorithms：Apriori algorithm、FP-growth algorithm
http://en.wikipedia.org/wiki/Association_rule_learning
Demo：Python + Orange
http://www.fuchaoqun.com/2008/08/data-mining-with-python-orange-association_rule/

Algorithms
Association Rules
Slope one
SVD
….

Slope One

Slope One
By Daniel Lemire in 2005
http://www.daniel-lemire.com/fr/abstracts/SDM2005.html
Simper Could Be Better
Weighted Average:
http://en.wikipedia.org/wiki/Slope_One
Implements: http://taste.sourceforge.net/ (Java)http://code.google.com/p/openslopeone (PHP&MySQL)

Algorithms
Association Rules
Slope one
SVD
….

Similarity
Similarity：

SVD
Image copy from Here

SVD In Image Compression
Original
K=10
K=20

Process SVD
Define the original user-item matrix, R, of size m x n, which includes the ratings of m users on n items. rij refers to the rating of user ui on item ij .
Preprocess user-item matrix R in order to eliminate all missing data values.
Compute the SVD of R and obtain matrices U, S and V , of size m x m, m x n, and n x n, respectively. Their relationship is expressed by: R =U * S * VT .
Perform the dimensionality reduction step by keeping only k diagonal entries from matrix S to obtain a k x k matrix, Sk. Similarly, matrices Uk and Vk of size m x k and k x n are generated. The "reduced" user-item matrix, R’, is obtained by R’ = Uk * Sk * VkT, while r'ij denotes the rating by user ui on item ij as included in this reduced matrix.
Compute sqrt(Sk) and then calculate two matrix products: Uk * sqrt(Sk)T, which represents m users and sqrt(Sk) * VkT , which represents n items in the k dimen-sional feature space. We are particularly interested in the latter matrix, of size k x n.
Use KNN on user matrix and item matrix, or you can multiply them to get user's rating on every item.

Demo
Which two people have the most similar tastes?
Which two season are the most close?
from Here

SVD
SVD
matlab
LAPCKL、BLAS (Fortran)
numpy、scipy (Python)
SVDLIBC、Meschach (C)
http://en.wikipedia.org/wiki/Singular_value_decomposition
……
KNN:
matlab
FLANN
……
All in one solution:
DIVISI
……

MAGIC DIVISI！
#!/usr/bin/env python
#coding=utf-8
import divisi
from divisi.cnet import *
data = divisi.SparseLabeledTensor(ndim = 2)
# read some rating into data
# data[user_id, song_id] = 4
svd_result = data.svd(k = 128)
# get songs that the user may like
# predict_features(svd_result, user_id).top_items(100)
# get similar songs
# feature_similarity(svd_result, song_id).top_items(100)
# get users that have similar tastes
# concept_similarity(svd_result, user_id).top_items(100)

Music Recommender Systems
Data collection
Data Cleaning
Data Preprocessing
Data Mining
Tracking & Optimization

Data collection
User rating
User collection
User listen log
User view log
….

Data Cleaning
Missing data
Wrong data
Noise data
Duplicate data
….

Data Preprocessing

Data Mining

Tracking & Optimization
Recommended result
User view and click what he like
Store user's click
Data Mining
Better recommendation

That's it, Thanks.
Q&A

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Music Recommender Systems

by fuchaoqun on Nov 22, 2009

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Music Recommender Systems — Presentation Transcript