profile shabazp/content based recommendation system

Updated Thu, September 21st - 06:14 AM GMT

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Install the Datmo CLI and clone this project.

$ datmo clone shabazp/content-based-recommendation-system
README.md

content based recommendation system

Datmo Model

In this example, we'll build an implicit feedback recommender using the Movielens 100k dataset (http://grouplens.org/datasets/movielens/100k/). The code behind this example is available as a Jupyter notebook LightFM includes functions for getting and processing this dataset, so obtaining it is quite easy.

In particular, it prepares the sparse user-item matrices, containing positive entries where a user interacted with a item, and zeros otherwise. We have two such matrices, a training and a testing set. Both have around 1000 users and 1700 items. We'll train the model on the train matrix but test it on the test matrix.

Snapshots

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Task: jupyter notebook

Session: default

Code: shabazbpatel/content-based-recommendation-system#b92839d6

Environment: Dockerfile

Files: None

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Task: jupyter notebook

Session: default

Code: shabazbpatel/content-based-recommendation-system#ce1cfd9d

Environment: Dockerfile

Files: None

Sessions

default

Snaphots: 2 2 months ago

Source Git Repository

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