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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.

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Collaborative Filtering Systems

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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

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Content-based Systems

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

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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

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# 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)

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

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

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