preferredai Goto Github PK

addvae

adjacent-encoder

Source code of the AAAI-2020 paper "Topic Modeling on Document Networks with Adjacent-Encoder"

alh

Implementation of "Augmenting Decision with Hypothesis in Reinforcement Learning"

beacon

The implementation of "Correlation-Sensitive Next-Basket Recommendation"", published in IJCAI'19

bi-vae

Code for the paper "Bilateral Variational Autoencoder for Collaborative Filtering", WSDM'21

bipoly

cbs

Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation

cerebro

Your personalized retrieval engine

cerebro-tutorial

A tutorial based on Cerebro

companion

Learning to Rank Aspects and Opinions for Comparative Explanations

compare-lda

A Topic Model for Document Comparison

comparer

Code of the paper "Explainable Recommendation with Comparative Constraints on Product Aspects", WSDM'21

comparesets

Code of the paper "Selecting Comparative Sets of Reviews Across Multiple Items", EDBT'25

core-technologies

Core technologies developed by Preferred.AI

cornac

A Comparative Framework for Multimodal Recommender Systems

cornac-ab

Recommendation A/B Testing Integration with Cornac

csvpl

CSV Processing Language

facetvae

geonn

helm-charts

Preferred.AI Helm Chart Repository

hypar

Code of the paper "Hypergraphs with Attention on Reviews for Explainable Recommendation", ECIR'24

integratedbundlepricing

mp-simrank

This repository provides a Matlab implementation of MP-SimRank, a graph-based framework that models multiple perspective of similarity in the data

mrg

Code for the paper "Multimodal Review Generation for Recommender Systems", WWW'19

plrmm

Plackett-Luce Regression Mixture Model

pybmf

A Python library for Boolean Matrix Factorization

quester

Code of the paper "Question-Attentive Review-Level Recommendation Explanation", BigData'22

recommendation-retrieval

A tutorial on scalable retrieval of matrix factorization recommendations

reintntm

ReIntNTM: A Tool for extracting better topics from neural topic models

seer

Code of the paper "Synthesizing Aspect-Driven Recommendation Explanations from Reviews", IJCAI'20