IESEGRecSys

Name	IESEGRecSys JSON
Version	0.23.8 JSON
	download
home_page	https://github.com/pnborchert
Summary	Recommendation Systems - IESEG School of Management
upload_time	2023-03-13 15:14:31
maintainer
docs_url	None
author	Philipp Borchert
requires_python
license	MIT
keywords	ieseg
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            <!-- DOCUMENT STYLE -->
<style>
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<!-- HEADER -->
<img src="https://www.ieseg.fr/wp-content/uploads/IESEG-Logo-2012-rgb.jpg" alt="drawing" style="width:50%;padding-bottom:1cm;"/>
<span style="float:right;">
    <br>
    Recommendation Systems
    <br>
    Module
    <br>
    Class: 2022 & 2023
</span>

<!-- CONTENT -->

---

## Package dependencies:
<center> 
 <a href="https://surpriselib.com/"><image src="https://surpriselib.com/logo_white.svg" width="30%"></a>
 <a href="https://scikit-learn.org/stable/"><image src="https://upload.wikimedia.org/wikipedia/commons/thumb/0/05/Scikit_learn_logo_small.svg/2560px-Scikit_learn_logo_small.svg.png" width="25%" style="padding-left:0.5cm;"></a>
</center>

---

## Overview

- Model evaluation (`eval.py`):
    - Regression metrics
        - RMSE
        - MAE
    - Classification metrics
        - Precision
        - Recall
        - F1
    - Ranking metrics
        - NDCG
    - `eval.evaluate` computes all above mentioned metrics 
- Content based Recommender System (`model.py`)
- Helper functions (`utils.py`)
    - `get_top_n`: Compute Top-N recommendations from predictions 
    - `predict_user_topn`: Compute Top-N recommendations for a user 

            

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