Recommendation systems In Machine Learning

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Recommendation Systems

Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.

Collaborative Filtering Systems

Collaborative filtering methods for recommender systems are methods that are solely based on the past interactions between users and the target items.

We can easily create a collaborative filtering recommender system using Graph Lab! We’ll take the following steps:

  1. Load up the data with pandas

  2. Convert the pandas data frames to graph lab SFrames

  3. Train the model

  4. Make recommendations

Content-based Systems

In contrast to collaborative filtering, content-based approaches will use additional information about the user and / or items to make predictions.

We can easily create a collaborative filtering recommender system using Graph Lab! We’ll take the following steps:

  1. Load up the data with pandas

  2. Convert the pandas data frames to graph lab SFrames

  3. Train the model

  4. Make recommendations

Implementation

import graphlab
import pandas as pd

# Load up the data with pandas
r_cols = ['user_id', 'food_item', 'rating']
train_data_df = pd.read_csv('train_data.csv', sep='\t', names=r_cols)
test_data_df = pd.read_csv('test_data.csv', sep='\t', names=r_cols)

# Convert the pandas dataframes to graph lab SFrames
train_data = graphlab.SFrame(train_data_df)
test_data = graphlab.SFrame(test_data_df)

# Train the model
cotent_filter_model = graphlab.item_content_recommender.create(train_data, 
                                                              user_id='user_id', 
                                                              item_id='food_item', 
                                                              target='rating')