haykinwu / recommendersystem-paper

This repository includes some papers that I have read or which I think may be very interesting.

Papers, tools , and framewroks that used in Recommender System

For the convenience of reading, I collect some basic and important papers about recommender system.

Here is the main conferences within recommender system and some categories which I think is interesting:

• KDD the community for data mining, data science and analytics.
• ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing.
• AAAI promotes research in, and responsible use of, artificial intelligence.
• WWW provides the world a premier forum for discussion and debate about the evolution of the Web, the standardization of its associated technologies, and the impact of those technologies on society and culture.
• NIPS has a responsibility to provide an inclusive and welcoming environment for everyone in the fields of AI and machine learning.
• ICML is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS).
• CIKM provides an international forum for presentation and discussion of research on information and knowledge management, as well as recent advances on data and knowledge bases.
• SIGIR is the Association for Computing Machinery’s Special Interest Group on Information Retrieval. Since 1963, we have promoted research, development and education in the area of search and other information access technologies.
• Recsys is the most famous conference in recommender system.
• Cold Start
  • 2017 TKDE
    • RaPare: A Generic Strategy for Cold-Start Rating Prediction Problem
  • 2017 IS
    • Local Representative-Based Matrix Factorization for Cold-Start Recommendation
  • 2017 AAAI
    • Low-Rank Linear Cold-Start Recommendation from Social Data
    • A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems
  • 2017 ACL
    • Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
  • 2017 IJCAI
    • Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness
  • 2017 Multimedia
    • Two Birds One Stone: On both Cold-Start and Long-Tail Recommendation
  • 2017 NIPS
    • A Meta-Learning Perspective on Cold-Start Recommendations for Items
    • DropoutNet: Addressing Cold Start in Recommender Systems
  • 2017 Recsys
    • Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations
  • 2017 WWW
    • On Learning Mixed Community-specific Similarity Metrics for Cold-start Link Prediction
• Survey
• Distribution
• Deep learning
  • 2017 ACML
    • PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems
      • Code Link: https://github.com/daicoolb/PHDMF
  • 2017 Recsys
    • Boosting Recommender Systems with Deep Learning
    • Deep Learning for Recommender Systems
    • TransNets: Learning to Transform for Recommendation by Rose Catherine
  • 2017 CORR
    • Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems by Mikhail Trofimov
  • 2017 IJCAI
    • DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.
      • Code Link: https://github.com/Leavingseason/OpenLearning4DeepRecsys
    • Deep Matrix Factorization Models for Recommender Systems
    • Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network
    • Cross-Domain Recommendation: An Embedding and Mapping Approach
    • Tag-Aware Personalized Recommendation Using a Hybrid Deep Model
  • 2017 WWW
    • Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems
    • What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation
    • Neural Collaborative Filtering
  • 2017 WSDM
    • Joint deep modeling of users and items using reviews for recommendation by L Zheng
  • 2017 KDD
    • Collaborative Variational Autoencoder for Recommender Systems
    • Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors Demonstration
    • A Hybrid Framework for Text Modeling with Convolutional RNN
    • Deep Embedding Forest: Forest-based Serving with Deep Embedding Features
    • Embedding-based News Recommendation for Millions of Users
  • 2017 SIGIR
    • Deep Learning for Extreme Multi-label Text Classification
    • Neural Factorization Machines for Sparse Predictive Analytics
    • Neural Rating Regression with Abstractive Tips Generation for Recommendation
    • Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention
  • 2016 Recsys
    • Convolutional Matrix Factorization for Document Context-Aware Recommendation by Donghyun Kim, Chanyoung Park, Jinoh Oh, Seungyong Lee, Hwanjo Yu
      • Code Link: https://github.com/cartopy/ConvMF
    • Ask the GRU: Multi-task Learning for Deep Text Recommendations by T Bansal
    • Deep Neural Networks for YouTube Recommendations by Paul Covington
    • Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation by Flavian Vasile
  • 2016 JMLR
    • A Neural Autoregressive Approach to Collaborative Filtering by Yin Zheng et all
  • 2016 ACML
    • Collaborative Recurrent Neural Networks for Dynamic Recommender Systems by Young-Jun Ko.
  • 2016 AAAI
    • Hybrid Recommender System based on Autoencoders by Florian Strub
  • 2016 WSDM Collaborative Denoising Auto-Encoders for Top-N Recommender Systems by Y Wu
  • 2016 DLRS Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng
  • 2016 Personal Note A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng
  • 2016 CORR
    • Collaborative Filtering with Recurrent Neural Networks by Robin Devooght
    • Hybrid Collaborative Filtering with Neural Networks by Strub
  • 2015 Recsys
    • Learning Distributed Representations from Reviews for Collaborative Filtering by Amjad Almahairi
  • 2015 WWW
    • A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems by Ali Mamdouh Elkahky
  • 2015 CIKM
    • Deep collaborative filtering via marginalized denoising auto-encoder by S Li
  • 2013 NIPS
    • Deep content-based music recommendation by Aaron van den Oord
  • 2007 ICML
    • Restricted Boltzmann Machines for Collaborative Filtering by Ruslan Salakhutdinov
• Talks
• etc

In this session, I have collected some useful recommeder system engine:

• Mosaic Mosaic Films is a demo of the recommendationRaccoon engine built on top of Node.js.
• Contenct Engine This is a production-ready, but very simple, content-based recommendation engine that computes similar items based on text descriptions.
• Spark Engine This tutorial shows how to run the code explained in the solution paper Recommendation Engine on Google Cloud Platform.
• Spring Boost How to build a recommendation engine with Spring Boot, Aerospike and MongoDB.
• Ger Providing good recommendations can get greater user engagement and provide an opportunity to add value that would otherwise not exist.
• Crab Crab as known as scikits.recommender is a Python framework for building recommender engines integrated with the world of scientific Python packages (numpy, scipy, matplotlib).

In this session, I have collected some useful recommender system algorithm framework:

• Surprise Surprise is a Python scikit building and analyzing recommender systems.
• LightFM LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses.
• SpotLight Spotlight uses PyTorch to build both deep and shallow recommender models.
• Python-Recsys A python library for implementing a recommender system.
• LibRec A java library for the state-of-the-art algorithms in recommeder sytem.
• SparkMovieLens A scalable on-line movie recommender using Spark and Flask.
• Elasticsearch Building a Recommender with Apache Spark & Elasticsearch.