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Recsys

Development: Overview

A Pass-through Policy
One pickle file from the OBP_Trainer.ipynb (right now is overfitted one user_ID to list, could be more nuance based on context to user_ID).

Data Cleaning

OFAiR Data Cleaning Notebook This notebook cleans the data similar to Sonboli’s cleaning data process (derived from Sonboli’s OFAiR project).
Sonboli’s OFAiR Github

Running Librec-auto

Install Librec-auto
Download Pycharm Community Version

Librec-auto Demos

Demo 01 to Demo 05: come built-in with Librec-auto

Results

Found in folder DemoOFAiR/Exp00000/result
The results for the movie dataset differ from Sonboli’s OFAiR paper because the author used different metrics code Sonboli OFAiR Repository
Librec-auto wasn’t used to calculate Sonboli’s metrics.

Development: Reranking

OBP Trainer

OBP Trainer Notebook Developed to provide a proof of concept. The learner uses the pass-through policy to take in as context user_ID in order to predict a Recommendation List (Similar results can be achieved by using a classification model).
1 user_ID → 1 recommendation list (is the nature of the design). This is somewhat impractical because the size of the resulting pickle file (i.e it is 44GB for the movie dataset). In other words, hard to scale.

How OBP stores data for the specific MovieDataSet

 'n_actions': 2830, # number of uniques itemids
    'context': array([0,1,2,3,4,5,...]), #userID
     #one action (itemID) for each position or 50*len(context)
    'action': array([8, 6, 5, 4, 7, 0, 1, 3, 5, 4, 6, 1, 4, 1, 7,...]), 
     #up to 50 positions for each context or 50*len(context)
    'position': array([0, 1, 2…49,50, 0, 1, 2…49,50,0, 1, 2…49,50,...]), 
 'reward': array([1, 1, 1, ..., 1, 1, 1]), #set all rewards to one

OBP: replace ofair_rerank.py with obp_rerank.py 

<!-- RERANK SECTION -->
  <rerank>
    <script lang="python3" src="system">
	<script-name>obp_rerank.py</script-name> 
	<param name="max_len">50</param>
	<param name="lambda"><value>0.9</value></param>
	<param name="binary">False</param>
	<param ref="fea:new"/>
    </script>
  </rerank>

To Debug: run the OBP_Ofair_Reranker wrapper in PyCharm

DEMO OBP Projects

The changes were made to the algorithm section of librec-auto. It was replaced with OBP. They require a pickle file (use the OBP Exporter notebook to create a pickle file). The following demo projects:

  1. DataOFAiR: was bult from the movie data. It contains the notebook to prepare the data. The movie data source is Kaggle.
  2. DemoOFAiR: changes to the re-ranking section two options: obp_rerank.py(added 04-2022) or ofair_rerank.py (already built-in librec-auto)
  3. DemoMMR: extension of OFAiR (same as OFAiR reranking but it uses MMR: already built-in librec-auto).

Development: Algorithm

OBP Exporter

Input: demoX-out-1.txt
Output: IPW_OBP_demoX.pickle
OBP Exporter Notebook

DEMO OBP Projects

The changes were made to the algorithm section of librec-auto. It was replaced with OBP. They require a pickle file (use the OBP Exporter notebook to create a pickle file). The following demo OBP projects:

  1. DemoOBP_STANDARD
  2. DemoOBP_RANDOM
  3. DemoOBP_FAIR FAIR STANDARD RANDOM Results calculated using OBP for pass-through policy (no evaluation, or dataset). Librec Auto for evaluation.

OBP Recommend Wrapper

obp_recommend_wrapper.py
Takes the user ID and gives a list on Librec auto.

  1. Passes the user into the OBP wrapper.
  2. Output is a list. Librec-auto during evaluation performs the metric