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
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

Introduction This is the repository of our accepted CIKM 2021 paper "Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Trans

SeqRec 29 Dec 09, 2022
Group-Buying Recommendation for Social E-Commerce

Group-Buying Recommendation for Social E-Commerce This is the official implementation of the paper Group-Buying Recommendation for Social E-Commerce (

Jun Zhang 37 Nov 28, 2022
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

57 Nov 03, 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
Fast Python Collaborative Filtering for Implicit Feedback Datasets

Implicit Fast Python Collaborative Filtering for Implicit Datasets. This project provides fast Python implementations of several different popular rec

Ben Frederickson 3k Dec 31, 2022
Plex-recommender - Get movie recommendations based on your current PleX library

plex-recommender Description: Get movie/tv recommendations based on your current

5 Jul 19, 2022
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer

Spotify 10.6k Jan 01, 2023
Codes for AAAI'21 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'

DHCN Codes for AAAI 2021 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'. Please note that the default link

Xin Xia 124 Dec 14, 2022
An Efficient and Effective Framework for Session-based Social Recommendation

SEFrame This repository contains the code for the paper "An Efficient and Effective Framework for Session-based Social Recommendation". Requirements P

Tianwen CHEN 23 Oct 26, 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
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
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
Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch

Recommendation engines are one of the most well known, widely used and highest value use cases for applying machine learning. Despite this, while there are many resources available for the basics of

International Business Machines 793 Dec 18, 2022
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.

COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype

Xin Xia 43 Jan 04, 2023
Bundle Graph Convolutional Network

Bundle Graph Convolutional Network This is our Pytorch implementation for the paper: Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin and Yong Li. Bun

55 Dec 25, 2022
Spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

A scalable on-line movie recommender using Spark and Flask This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens datase

Jose A Dianes 794 Dec 23, 2022
A tensorflow implementation of the RecoGCN model in a CIKM'19 paper, titled with "Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation".

This repo contains a tensorflow implementation of RecoGCN and the experiment dataset Running the RecoGCN model python train.py Example training outp

xfl15 30 Nov 25, 2022
A TensorFlow recommendation algorithm and framework in Python.

TensorRec A TensorFlow recommendation algorithm and framework in Python. NOTE: TensorRec is not under active development TensorRec will not be receivi

James Kirk 1.2k Jan 04, 2023
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
This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

GHCF This is our implementation of the paper: Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu and Shaoping Ma. 2

Chong Chen 53 Dec 05, 2022