Mutual Fund Recommender System. Tailor for fund transactions.

Related tags

Recommender Systemsfund_recsys
Overview

Explainable Mutual Fund Recommendation

Data

Please see 'DATA_DESCRIPTION.md' for mode detail.

Recommender System Methods

Baseline

  • Collabarative Fiiltering
  • PersonFreq
  • PersonVolume

Stable

  • LightFM Meta
  • LightFM PureCF
  • LightFM Hybrid

Advanced

  • DGL
  • GCN

Part I: Fund Recommedation

Training

Supported models
  1. Heuristic
  2. LightFM (CF/Hybrid/Meta)
  3. SMORe
# Process 3 models in parallel
bash run_all.sh
Arugments

You can also tune the detail parameter settings of each method in training pipeline.

--use_heuristic "> 
# Commonly used arguments 
--model 
    
     
--model_type 
     
      
--model_hidden_dimension 
      
       
--evaluation_metrics 
       
        
--use_heuristic

For example, LightFM with pure-CF method

EPOCHS=10
EMBED_SIZE=64
DATE=20181231

python3 train.py \
   --path_transaction data/${DATE}/transaction_train.csv \
   --path_transaction_eval data/${DATE}/transaction_eval.csv \
   --path_user data/${DATE}/customer.csv \
   --path_item data/${DATE}/product.csv \
   --model 'LightFM' \
   --model_path 'models/lightfm' \
   --model_type 'cf' \
   --model_hidden_dimension ${EMBED_SIZE} \
   --model_max_neg_sample 100 \
   --model_loss 'warp' \
   --training_do_evaluation \
   --training_verbose \
   --training_num_epochs ${EPOCHS} \
   --training_eval_per_epochs 1 \
   --evaluation_diff \
   --evaluation_regular \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --use_heuristic 'frequency' \
   --use_heuristic 'volume' \
   --evaluation_results_csv results/lightfm_cf_evaluation_${DATE}.csv \
   --evaluation_rec_detail_report results/lightfm_cf_rec_detail_${DATE}.tsv \
       > logs/lightfm_cf_exp_${DATE}.log

For another example, SMORe

python3 train.py \
   --path_transaction data/${DATE}/transaction_train.csv \
   --path_transaction_eval data/${DATE}/transaction_eval.csv \
   --path_user data/${DATE}/customer.csv \
   --path_item data/${DATE}/product.csv \
   --model 'SMORe' \
   --model_path 'models/smore' \
   --model_hidden_dimension ${EMBED_SIZE} \
   --model_max_neg_sample 100 \
   --model_loss 'warp' \
   --training_do_ \
   --training_verbose \
   --training_num_epochs ${EPOCHS} \
   --training_eval_per_epochs 1 \
   --evaluation_diff \
   --evaluation_regular \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_results_csv results/smore_evaluation_${DATE}.csv \
   --evaluation_rec_detail_report results/smore_rec_detail_${DATE}.tsv \
       > logs/smore_exp_${DATE}.log

Evaluataion

To use the evaluation pipeline, you need a prediction rec file with the format like the example below:

# prediction rec file 
   
    \t
    
     \t
     
      \t
      
       \t
       
        \t
        
          CFDAXWccjJPoVInuiF0mMg== AG25 EXPLOIT SOLO 0 2 CFDAXWccjJPoVInuiF0mMg== XXXX EXPLOIT SOLO 0 1 CFDAXWccjJPoVInuiF0mMg== JJ15 EXPLOIT REGULAR 0 2 CFDAXWccjJPoVInuiF0mMg== XXXX EXPLOIT REGULAR 0 1 CFDAwH4y/ssuYSedFy8UMw== CC89 EXPLOIT REGULAR 0 2 CFDAwH4y/ssuYSedFy8UMw== XXXX EXPLOIT REGULAR 0 1 CFDA9UDJnLAm4/0txbPMVQ== AP06 EXPLORE NA 0 2 CFDA9UDJnLAm4/0txbPMVQ== XXXX EXPLORE NA 0 1

Later you could directly use the evaluate pipeline

bash rec_convert_eval.sh

In the evaluation pipeline, you need to convert the ground truth interaction into '.rec' format. For xample.

# truth rec file 
   
    \t
    
     \t
     
      \t
      
       \t
       
         CFDAXWccjJPoVInuiF0mMg== AG25 EXPLOIT SOLO 1.0 CFDAXWccjJPoVInuiF0mMg== JJ15 EXPLOIT REGULAR 1.0 CFDAwH4y/ssuYSedFy8UMw== CC89 EXPLOIT REGULAR 1.0 CFDA9UDJnLAm4/0txbPMVQ== AP06 EXPLORE NA 1.0

Convert from the evaluation transaction (includes the preprocess pipeline) by the following code, which will save the corresponding rec file in the defined argument '--path_trainsaction_truth'

DATE=20181231
python3 convert_to_rec.py \
    --path_transaction data/${DATE}/transaction_train.csv \
    --path_transaction_eval data/${DATE}/transaction_eval.csv \
    --path_user data/${DATE}/customer.csv \
    --path_item data/${DATE}/product.csv \
    --path_transaction_truth rec/${DATE}.eval.truth.rec

And evaluate by the code "rec_eval.py"

DATE=20181231
python3 rec_eval.py \
   -truth rec/${DATE}.eval.truth.rec \ 
   -pred rec/pred.rec \     
   -metric '[email protected]' \          
   -metric '[email protected]' \          
   -metric '[email protected]' \
   -metric '[email protected]'

The results would be like

TRUTH REC FILE EXISTED:  'rec/20181231.eval.truth.rec'

EvalDict({                
          SUBSET     USERS     EXAMPLES 
        * EXPLORE    2305      2826     
        * EXPLOIT    33355     62403    
        * REGULAR    31763     59054    
        * SOLO       2747      3349                     
})
==============================
 [email protected]     on EXPLORE    0.0001
 [email protected]     on EXPLORE    0.0004
 [email protected]   on EXPLORE    0.0004
 [email protected]   on EXPLORE    0.0004
 [email protected]     on EXPLOIT    0.0000
 [email protected]     on EXPLOIT    0.0001
 [email protected]   on EXPLOIT    0.0001
 [email protected]   on EXPLOIT    0.0001
 [email protected]     on REGULAR    0.0000
 [email protected]     on REGULAR    0.0001
 [email protected]   on REGULAR    0.0001
 [email protected]   on REGULAR    0.0001
 [email protected]     on SOLO       0.0001
 [email protected]     on SOLO       0.0004
 [email protected]   on SOLO       0.0004
 [email protected]   on SOLO       0.0004
==============================

Results

Methods [email protected] [email protected] [email protected] [email protected]
Collabarative Fiiltering - - -
PersonFreq - - -
PersonVolume - - -
LightFM Meta - - -
LightFM PureCF - - -
LightFM Hybrid 0.000 0.000 0.000 0.000
DGL - - -
GCN - - -

Fund Explanation

Owner
JHJu
Research assistant @ cnc Lab, ASCITI
JHJu
GitHub Repository
A library of metrics for evaluating recommender systems

recmetrics A python library of evalulation metrics and diagnostic tools for recommender systems. **This library is activly maintained. My goal is to c

Claire Longo 458 Jan 06, 2023
Cross-Domain Recommendation via Preference Propagation GraphNet.

PPGN Codes for CIKM 2019 paper Cross-Domain Recommendation via Preference Propagation GraphNet. Citation Please cite our paper if you find this code u

Information Retrieval Group, Wuhan University, China 20 Dec 15, 2022
E-Commerce recommender demo with real-time data and a graph database

🔍 E-Commerce recommender demo 🔍 This is a simple stream setup that uses Memgraph to ingest real-time data from a simulated online store. Data is str

g-despot 3 Feb 23, 2022
Pytorch domain library for recommendation systems

TorchRec (Experimental Release) TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale

Meta Research 1.3k Jan 05, 2023
Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher's perspective.

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Information Systems Lab @ Polytechnic University of Bari 215 Nov 29, 2022
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation, SIGIR 2020

hierarchical_fashion_graph_network This is our Tensorflow implementation for the paper: Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, and

LI Xingchen 70 Dec 05, 2022
Code for KHGT model, AAAI2021

KHGT Code for KHGT accepted by AAAI2021 Please unzip the data files in Datasets/ first. To run KHGT on Yelp data, use python labcode_yelp.py For Movi

32 Nov 29, 2022
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and newly state-of-the-art recommendation models are implemented.

Yu 1.4k Dec 27, 2022
Recommender systems are the systems that are designed to recommend things to the user based on many different factors

Recommender systems are the systems that are designed to recommend things to the user based on many different factors. The recommender system deals with a large volume of information present by filte

Happy N. Monday 3 Feb 15, 2022
Graph Neural Networks for Recommender Systems

This repository contains code to train and test GNN models for recommendation, mainly using the Deep Graph Library (DGL).

217 Jan 04, 2023
Self-supervised Graph Learning for Recommendation

SGL This is our Tensorflow implementation for our SIGIR 2021 paper: Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian,and Xing

151 Dec 20, 2022
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021

Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN) This is our implementation for the paper: Su, Y., Zhang, R., Erfani, S., &

26 Nov 22, 2022
Recommender System Papers

Included Conferences: SIGIR 2020, SIGKDD 2020, RecSys 2020, CIKM 2020, AAAI 2021, WSDM 2021, WWW 2021

RUCAIBox 704 Jan 06, 2023
Code for ICML2019 Paper "Compositional Invariance Constraints for Graph Embeddings"

Dependencies NOTE: This code has been updated, if you were using this repo earlier and experienced issues that was due to an outaded codebase. Please

Avishek (Joey) Bose 43 Nov 25, 2022
Books Recommendation With Python

Books-Recommendation Business Problem During the last few decades, with the rise

Çağrı Karadeniz 7 Mar 12, 2022
Recommendation System to recommend top books from the dataset

recommendersystem Recommendation System to recommend top books from the dataset Introduction The recom.py is the main program code. The dataset is als

Vishal karur 1 Nov 15, 2021
A library of Recommender Systems

A library of Recommender Systems This repository provides a summary of our research on Recommender Systems. It includes our code base on different rec

MilaGraph 980 Jan 05, 2023
Cloud-based recommendation system

This project is based on cloud services to create data lake, ETL process, train and deploy learning model to implement a recommendation system.

Yi Ding 1 Feb 02, 2022
Attentive Social Recommendation: Towards User And Item Diversities

ASR This is a Tensorflow implementation of the paper: Attentive Social Recommendation: Towards User And Item Diversities Preprint, https://arxiv.org/a

Dongsheng Luo 1 Nov 14, 2021
This library intends to be a reference for recommendation engines in Python

Crab - A Python Library for Recommendation Engines

Marcel Caraciolo 85 Oct 04, 2021