Recommender System Papers

Overview

Awesome-RSPapers

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

Task

Collaborative Filtering

  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR 2020

  • Deep Critiquing for VAE-based Recommender Systems. SIGIR 2020

  • Neighbor Interaction Aware Graph Convolution Networks for Recommendation. SIGIR 2020

  • A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks. KDD 2020

  • Dual Channel Hypergraph Collaborative Filtering. KDD 2020

  • Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation. KDD 2020

  • Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. RecSys 2020

  • Neural Collaborative Filtering vs. Matrix Factorization Revisited. RecSys 2020

  • Bilateral Variational Autoencoder for Collaborative Filtering. WSDM 2021

  • Learning User Representations with Hypercuboids for Recommender Systems. WSDM 2021

  • Local Collaborative Autoencoders. WSDM 2021

  • A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion. WWW 2021

  • HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. WWW 2021

  • High-dimensional Sparse Embeddings for Collaborative Filtering. WWW 2021

  • Collaborative Filtering with Preferences Inferred from Brain Signals. WWW 2021

  • Interest-aware Message-Passing GCN for Recommendation. WWW 2021

  • Neural Collaborative Reasoning. WWW 2021

  • Sinkhorn Collaborative Filtering. WWW 2021

  • Disentangling User Interest and Conformity for Recommendation with Causal Embedding. WWW 2021

Sequential/Session-based Recommendations

  • Sequential Recommendation with Self-attentive Multi-adversarial Network. SIGIR 2020

  • KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation. SIGIR 2020

  • Modeling Personalized Item Frequency Information for Next-basket Recommendation. SIGIR 2020

  • Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation. SIGIR 2020

  • GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation. SIGIR 2020

  • Next-item Recommendation with Sequential Hypergraphs. SIGIR 2020

  • A General Network Compression Framework for Sequential Recommender Systems. SIGIR 2020

  • Make It a Chorus: Knowledge- and Time-aware Item Modeling for Sequential Recommendation. SIGIR 2020

  • Global Context Enhanced Graph Neural Networks for Session-based Recommendation. SIGIR 2020

  • Self-Supervised Reinforcement Learning for Recommender Systems. SIGIR 2020

  • Time Matters: Sequential Recommendation with Complex Temporal Information. SIGIR 2020

  • Controllable Multi-Interest Framework for Recommendation. KDD 2020

  • Disentangled Self-Supervision in Sequential Recommenders. KDD 2020

  • Handling Information Loss of Graph Neural Networks for Session-based Recommendation. KDD 2020

  • Contextual and Sequential User Embeddings for Large-Scale Music Recommendation. RecSys 2020

  • FISSA:Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation. RecSys 2020

  • From the lab to production: A case study of session-based recommendations in the home-improvement domain. RecSys 2020

  • Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de. RecSys 2020

  • SSE-PT:Sequential Recommendation Via Personalized Transformer. RecSys 2020

  • Improving End-to-End Sequential Recommendations with Intent-aware Diversification. CIKM 2020

  • Quaternion-based self-Attentive Long Short-term User Preference Encoding for Recommendation. CIKM 2020

  • Sequential Recommender via Time-aware Attentive Memory Network. CIKM 2020

  • Star Graph Neural Networks for Session-based Recommendation. CIKM 2020

  • Dynamic Memory Based Attention Network for Sequential Recommendation. AAAI 2021

  • Noninvasive Self-Attention for Side Information Fusion in Sequential Recommendation. AAAI 2021

  • Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. AAAI 2021

  • An Efficient and Effective Framework for Session-based Social Recommendation. WSDM 2021

  • Sparse-Interest Network for Sequential Recommendation. WSDM 2021

  • Dynamic Embeddings for Interaction Prediction. WWW 2021

  • Session-aware Linear Item-Item Models for Session-based Recommendation. WWW 2021

  • RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation. WWW 2021

  • Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation. WWW 2021

  • Future-Aware Diverse Trends Framework for Recommendation. WWW 2021

  • DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce. WWW 2021

  • Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation. WWW 2021

Knowledge-aware Recommendations

  • CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems. SIGIR 2020

  • Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View. SIGIR 2020

  • MVIN: Learning multiview items for recommendation. SIGIR 2020

  • Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation. SIGIR 2020

  • Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach. SIGIR 2020

  • Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs. SIGIR 2020

  • SimClusters Community-Based Representations for Heterogenous Recommendations at Twitter. KDD 2020

  • Multi-modal Knowledge Graphs for Recommender Systems. CIKM 2020

  • DisenHAN Disentangled Heterogeneous Graph Attention Network for Recommendation. CIKM 2020

  • Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network. CIKM 2020

  • TGCN Tag Graph Convolutional Network for Tag-Aware Recommendation. CIKM 2020

  • Knowledge-Enhanced Top-K Recommendation in Poincaré Ball. AAAI 2021

  • Graph Heterogeneous Multi-Relational Recommendation. AAAI 2021

  • Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. AAAI 2021

  • Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph. WSDM2021

  • Decomposed Collaborative Filtering Modeling Explicit and Implicit Factors For Recommender Systems. WSDM 2021

  • Temporal Meta-path Guided Explainable Recommendation. WSDM 2021

  • Learning Intents behind Interactions with Knowledge Graph for Recommendation. WWW 2021

Feature Interactions

  • Detecting Beneficial Feature Interactions for Recommender Systems. AAAI 2021

  • DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. WSDM 2021

  • Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction. WSDM 2021

  • FM^2: Field-matrixed Factorization Machines for CTR Prediction. WWW 2021

Conversational Recommender System

  • Towards Question-based Recommender Systems. SIGIR 2020

  • Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion. KDD 2020

  • Interactive Path Reasoning on Graph for Conversational Recommendation. KDD 2020

  • A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems. RecSys 2020

  • What does BERT know about books, movies and music: Probing BERT for Conversational Recommendation. RecSys 2020

  • Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation. WSDM 2021

  • A Workflow Analysis of Context-driven Conversational Recommendation. WWW 2021

Social Recommendations

  • Partial Relationship Aware Influence Diffusion via a Multi-channel Encoding Scheme for Social Recommendation. CIKM 2020

  • Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks. WWW 2021

  • Dual Side Deep Context-aware Modulation for Social Recommendation. WWW 2021

  • Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. WWW 2021

News Recommendation

  • KRED:Knowledge-Aware Document Representation for News Recommendations. RecSys 2020

  • News Recommendation with Topic-Enriched Knowledge Graphs. CIKM 2020

  • The Interaction between Political Typology and Filter Bubbles in News Recommendation Algorithms. WWW 2021

Text-aware Recommendations

  • TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations. RecSys 2020

  • Set-Sequence-Graph A Multi-View Approach Towards Exploiting Reviews for Recommendation. CIKM 2020

  • TPR: Text-aware Preference Ranking for Recommender Systems. CIKM 2020

  • Leveraging Review Properties for Effective Recommendation. WWW 2021

POI

  • HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation. SIGIR 2020

  • Spatial Object Recommendation with Hints: When Spatial Granularity Matters. SIGIR 2020

  • Geography-Aware Sequential Location Recommendation. KDD 2020

  • Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation. CIKM 2020

  • STP-UDGAT Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation. CIKM 2020

  • STAN: Spatio-Temporal Attention Network for next Point-of-Interest Recommendation. WWW 2021

  • Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching. WWW 2021

Online Recommendations

  • Gemini: A novel and universal heterogeneous graph information fusing framework for online recommendations. KDD 2020

  • Maximizing Cumulative User Engagement in Sequential Recommendation An Online Optimization Perspective. KDD 2020

  • Exploring Clustering of Bandits for Online Recommendation System. RecSys 2020

  • Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation. RecSys 2020

  • A Hybrid Bandit Framework for Diversified Recommendation. AAAI 2021

Group Recommendation

  • GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation. SIGIR 2020

  • GroupIM: A Mutual Information Maximizing Framework for Neural Group Recommendation. SIGIR 2020

  • Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation. SIGIR 2020

Multi-task/Multi-behavior/Cross-domain Recommendations

  • Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation. SIGIR 2020

  • CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network. SIGIR 2020

  • Multi-behavior Recommendation with Graph Convolution Networks. SIGIR 2020

  • Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. SIGIR 2020

  • Web-to-Voice Transfer for Product Recommendation on Voice. SIGIR 2020

  • Jointly Learning to Recommend and Advertise. KDD 2020

  • Progressive Layered Extraction (PLE) A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. RecSys 2020

  • Whole-Chain Recommendations. CIKM 2020

  • Personalized Approximate Pareto-Efficient Recommendation. WWW 2021

Other Task

  • Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. SIGIR 2020

  • Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates. SIGIR 2020

  • Goal-driven Command Recommendations for Analysts. RecSys 2020

  • MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems. RecSys 2020

  • PURS: Personalized Unexpected Recommender System for Improving User Satisfaction. RecSys 2020

  • RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues. RecSys 2020

  • Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization. CIKM 2020

  • Learning to Recommend from Sparse Data via Generative User Feedback. AAAI 2021

  • Real-time Relevant Recommendation Suggestion. WSDM 2021

  • Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks. WSDM 2021

  • FINN: Feedback Interactive Neural Network for Intent Recommendation. WWW 2021

  • Drug Package Recommendation via Interaction-aware Graph Induction. WWW 2021

  • Large-scale Comb-K Recommendation. WWW 2021

  • Variation Control and Evaluation for Generative Slate Recommendations. WWW 2021

  • Diversified Recommendation Through Similarity-Guided Graph Neural Networks. WWW 2021

Topic

Debias in Recommender System

  • A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. SIGIR 2020

  • Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. SIGIR 2020

  • Attribute-based Propensity for Unbiased Learning in Recommender Systems Algorithm and Case Studies. KDD 2020

  • Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions. KDD 2020

  • Debiasing Item-to-Item Recommendations With Small Annotated Datasets. RecSys 2020

  • Keeping Dataset Biases out of the Simulation : A Debiased Simulator for Reinforcement Learning based Recommender Systems. RecSys 2020

  • Unbiased Ad Click Prediction for Position-aware Advertising Systems. RecSys 2020

  • Unbiased Learning for the Causal Effect of Recommendation. RecSys 2020

  • E-commerce Recommendation with Weighted Expected Utility. CIKM 2020

  • Popularity-Opportunity Bias in Collaborative Filtering. WSDM 2021

  • Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings. WSDM 2021

  • Leave No User Behind Towards Improving the Utility of Recommender Systems for Non-mainstream Users. WSDM 2021

  • Non-Clicks Mean Irrelevant Propensity Ratio Scoring As a Correction. WSDM 2021

  • Diverse User Preference Elicitation with Multi-Armed Bandits. WSDM 2021

  • Unbiased Learning to Rank in Feeds Recommendation. WSDM 2021

  • Cross-Positional Attention for Debiasing Clicks. WWW 2021

  • Debiasing Career Recommendations with Neural Fair Collaborative Filtering. WWW 2021

Fairness in Recommender System

  • Fairness-Aware Explainable Recommendation over Knowledge Graphs. SIGIR 2020

  • Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance. RecSys 2020

  • Fairness-Aware News Recommendation with Decomposed Adversarial Learning. AAAI 2021 news

  • Practical Compositional Fairness Understanding Fairness in Multi-Component Recommender Systems. WSDM 2021

  • Towards Long-term Fairness in Recommendation. WSDM 2021

  • Learning Fair Representations for Recommendation: A Graph-based Perspective. WWW 2021

  • User-oriented Group Fairness In Recommender Systems. WWW 2021

Attack in Recommender System

  • Revisiting Adversarially Learned Injection Attacks Against Recommender Systems. RecSys 2020

  • Attacking Recommender Systems with Augmented User Profiles. CIKM 2020

  • A Black-Box Attack Model for Visually-Aware Recommenders. WSDM 2021

  • Denoising Implicit Feedback for Recommendation. WSDM 2021

  • Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start. WWW 2021

  • Graph Embedding for Recommendation against Attribute Inference Attacks. WWW 2021

Explanation in Recommender System

  • Try This Instead: Personalized and Interpretable Substitute Recommendation. KDD 2020

  • CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation. CIKM 2020

  • Explainable Recommender Systems via Resolving Learning Representations. CIKM 2020

  • Generate Neural Template Explanations for Recommendation. CIKM 2020

  • Explainable Recommendation with Comparative Constraints on Product Aspects. WSDM 2021

  • Explanation as a Defense of Recommendation. WSDM 2021

  • EX^3: Explainable Product Set Recommendation for Comparison Shopping. WWW 2021

  • Learning from User Feedback on Explanations to Improve Recommender Models. WWW 2021

Long-tail/Cold-start in Recommendations

  • Content-aware Neural Hashing for Cold-start Recommendation. SIGIR 2020

  • MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation. KDD 2020

  • Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling. KDD 2020

  • Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. KDD 2020

  • Cold-Start Sequential Recommendation via Meta Learner. AAAI 2021

  • Personalized Adaptive Meta Learning for Cold-start User Preference Prediction. AAAI 2021

  • Task-adaptive Neural Process for User Cold-Start Recommendation. WWW 2021

  • A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation. WWW 2021

Evaluation

  • Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations. SIGIR 2020

  • Evaluating Conversational Recommender Systems via User Simulation. KDD 2020

  • On Sampled Metrics for Item Recommendation. KDD 2020

  • On Sampling Top-K Recommendation Evaluation. KDD 2020

  • Are We Evaluating Rigorously: Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison. RecSys 2020

  • On Target Item Sampling in Offline Recommender System Evaluation. RecSys 2020

Technique

Pre-training in Recommender System

  • S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. CIKM 2020

  • U-BERT Pre-Training User Representations for Improved Recommendation. AAAI 2021

  • Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation. WSDM 2021

Reinforcement Learning in Recommendations

  • MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations. SIGIR 2020

  • Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning. SIGIR 2020

  • Joint Policy-Value Learning for Recommendation. KDD 2020

  • BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. KDD 2020

  • Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication. RecSys 2020

  • Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation. AAAI 2021

  • User Response Models to Improve a REINFORCE Recommender System. WSDM 2021

  • Cost-Effective and Interpretable Job Skill Recommendation with Deep Reinforcement Learning. WWW 2021

  • A Multi-Agent Reinforcement Learning Framework for Intelligent Electric Vehicle Charging Recommendation. WWW 2021

  • Reinforcement Recommendation with User Multi-aspect Preference. WWW 2021

Knowledge Distillation in Recommendations

  • Privileged Features Distillation at Taobao Recommendations. KDD 2020

  • DE-RRD: A Knowledge Distillation Framework for Recommender System. CIKM 2020

  • Bidirectional Distillation for Top-K Recommender System. WWW 2021

NAS in Recommendations

  • Neural Input Search for Large Scale Recommendation Models. KDD 2020

  • Field-aware Embedding Space Searching in Recommender Systems. WWW 2021

Federated Learning in Recommendations

  • FedFast Going Beyond Average for Faster Training of Federated Recommender Systems. KDD 2020

Analysis

  • How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models. SIGIR 2020

  • Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation. SIGIR 2020

  • Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CIKM 2020

  • On Estimating Recommendation Evaluation Metrics under Sampling. AAAI 2021

  • Beyond Point Estimate Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems. WSDM 2021

  • Bias-Variance Decomposition for Ranking. WSDM 2021

  • Theoretical Understandings of Product Embedding for E-commerce Machine Learning. WSDM 2021

Other

  • Learning Personalized Risk Preferences for Recommendation. SIGIR 2020

  • Distributed Equivalent Substitution Training for Large-Scale Recommender Systems. SIGIR 2020

  • Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation. SIGIR 2020

  • How to Retrain a Recommender System? SIGIR 2020

  • Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. SIGIR 2020

  • Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. KDD 2020

  • Improving Recommendation Quality in Google Drive. KDD 2020

  • A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets. RecSys 2020

  • Exploiting Performance Estimates for Augmenting Recommendation Ensembles. RecSys 2020

  • User Simulation via Supervised Generative Adversarial Network. WWW 2021

Owner
RUCAIBox
An enthusiastic group that aims to create beautiful things with AI
RUCAIBox
Persine is an automated tool to study and reverse-engineer algorithmic recommendation systems.

Persine, the Persona Engine Persine is an automated tool to study and reverse-engineer algorithmic recommendation systems. It has a simple interface a

Jonathan Soma 87 Nov 29, 2022
A Python implementation of LightFM, a hybrid recommendation algorithm.

LightFM Build status Linux OSX (OpenMP disabled) Windows (OpenMP disabled) LightFM is a Python implementation of a number of popular recommendation al

Lyst 4.2k Jan 02, 2023
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
A recommendation system for suggesting new books given similar books.

Book Recommendation System A recommendation system for suggesting new books given similar books. Datasets Dataset Kaggle Dataset Notebooks goodreads-E

Sam Partee 2 Jan 06, 2022
Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Bi-TGCF Tensorflow Implementation of BiTGCF: Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks. in CIKM20

17 Nov 30, 2022
RecList is an open source library providing behavioral, "black-box" testing for recommender systems.

RecList is an open source library providing behavioral, "black-box" testing for recommender systems.

Jacopo Tagliabue 375 Dec 30, 2022
The implementation of the submitted paper "Deep Multi-Behaviors Graph Network for Voucher Redemption Rate Prediction" in SIGKDD 2021 Applied Data Science Track.

DMBGN: Deep Multi-Behaviors Graph Networks for Voucher Redemption Rate Prediction The implementation of the accepted paper "Deep Multi-Behaviors Graph

10 Jul 12, 2022
Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation

MKM-SR Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation Paper data and code This is the

ciecus 38 Dec 05, 2022
fastFM: A Library for Factorization Machines

Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat

1k Dec 24, 2022
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation This repository contains the source code of the SIGIR 2019 paper "Reinforcement

Yikun Xian 197 Dec 28, 2022
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
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
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
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
Implementation of a hadoop based movie recommendation system

Implementation-of-a-hadoop-based-movie-recommendation-system 通过编写代码,设计一个基于Hadoop的电影推荐系统,通过此推荐系统的编写,掌握在Hadoop平台上的文件操作,数据处理的技能。windows 10 hadoop 2.8.3 p

汝聪(Ricardo) 5 Oct 02, 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
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
大规模推荐算法库,包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、DeepWalk、SSR、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、ListWise等

(中文文档|简体中文|English) 什么是推荐系统? 推荐系统是在互联网信息爆炸式增长的时代背景下,帮助用户高效获得感兴趣信息的关键; 推荐系统也是帮助产品最大限度吸引用户、留存用户、增加用户粘性、提高用户转化率的银弹。 有无数优秀的产品依靠用户可感知的推荐系统建立了良好的口碑,也有无数的公司依

3.6k Dec 30, 2022
Mutual Fund Recommender System. Tailor for fund transactions.

Explainable Mutual Fund Recommendation Data Please see 'DATA_DESCRIPTION.md' for mode detail. Recommender System Methods Baseline Collabarative Fiilte

JHJu 2 May 19, 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