Machine Learning Project Recommender System

Ryan D. Moniz

Clear Goal

• Recommender System for Music.

• e.g. similar to Amazon’s Recommendation System

• similar to iTunes’ Recommendation system

Amazon’s Collaborative Filtering System

iTunes’ Recommender System

Non-Trivial Problem

• Compared with others who bought the same CD or song as you.

Non-Trivial Problem

• Your entire music library compared with everyone who bought the same CD or song as you.

Attributes

• Artist

• Album

• Beats Per Minute

• Genre

• Year

Learning Method

• ART1 - Adaptive Resonance Theory

• Applied to dynamically self-organizing data.

• Unsupervised clustering neural network.

• Incremental but stable clusters.

Learning Method

• Step 1 - Initialization

• Initialize N to the total number of clusters

• Initialize the vigilance parameter p so 0 < p ≤ 1.

• Let p = (0 ∪ 1)* ∀i ∈ [1,N]i

Learning Method

• Step 2 - Apply new input vector

• Let I = [next input vector]

• Let P’ = P be the set of candidate prototype vectors

Learning Method

• Step 3 - Find the closest prototype vector from P’

• Find the i which maximizes

β +

|| p ∪ I ||

||p ||i

i

|| p ∩ I ||

|| I ||< p then P’ = P’ -

Learning Method

• Step 4

• Check if is too far from I

• If

• If P’ is empty goto step 2 otherwise goto step 3.

pi

pi

Learning Method

• Step 5 - Update the matched prototype vector

• Let = ∩ I ; output i; goto step 2.

• β acts a tie-breaker favoring the larger magnitude prototype vectors.

pi

pi

Learning Method

Learning Method

Principle Way to Evaluate

• Recommend relevant songs, albums, artists, genres (better prediction).

• e.g. if you like classical music and have no heavy metal in your collection you probably don’t prefer heavy metal.

≠

Progress

• Algorithm is “almost” implemented.

• Parameter tweaking will need to be done from time to time. (β, p, and d).

• Need to find a way to add lots of data, e.g. Music libraries/databases.

Risk

• Finding enough data for training & evaluation.

• Time to complete project.