# RecommenderSystem-Paper **Repository Path**: mirrors_daicoolb/RecommenderSystem-Paper ## Basic Information - **Project Name**: RecommenderSystem-Paper - **Description**: This repository includes some papers that I have read or which I think may be very interesting. - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2022-01-11 - **Last Updated**: 2025-12-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## Papers, tools , and framewroks that used in Recommender System [![](https://jaywcjlove.github.io/sb/ico/awesome.svg)](#) [![](https://jaywcjlove.github.io/sb/license/mit.svg)](#) For the convenience of reading, I collect some basic and important papers about recommender system. **Here are the main conferences within recommender system**: - [KDD](http://www.kdd.org/) the community for data mining, data science and analytics. - [ICDM](http://www.cs.uvm.edu/~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](https://www.aaai.org/) promotes research in, and responsible use of, artificial intelligence. - [WWW](http://www.iw3c2.org/) 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](https://nips.cc/) has a responsibility to provide an inclusive and welcoming environment for everyone in the fields of AI and machine learning. - [ICML](https://icml.cc/) is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS). - [CIKM](http://www.cikmconference.org/) 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](http://sigir.org/) 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](https://recsys.acm.org/) is the most famous conference in recommender system. - [WSDM](http://www.wsdm-conference.org/) (pronounced "wisdom") is one of the the premier conferences on web inspired research involving search and data mining. **In this session, I have collected some useful recommeder system engine**: - [Mosaic](https://github.com/guymorita/Mosaic-Films---Recommendation-Engine-Demo) Mosaic Films is a demo of the recommendationRaccoon engine built on top of Node.js. - [Contenct Engine](https://github.com/groveco/content-engine) This is a production-ready, but very simple, content-based recommendation engine that computes similar items based on text descriptions. - [Spark Engine](https://github.com/GoogleCloudPlatform/spark-recommendation-engine) This tutorial shows how to run the code explained in the solution paper Recommendation Engine on Google Cloud Platform. - [Spring Boost](https://github.com/aerospike/recommendation-engine-example) How to build a recommendation engine with Spring Boot, Aerospike and MongoDB. - [Ger](https://github.com/grahamjenson/ger) Providing good recommendations can get greater user engagement and provide an opportunity to add value that would otherwise not exist. - [Crab](https://muricoca.github.io/crab/index.html) 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](https://github.com/NicolasHug/Surprise) Surprise is a Python scikit building and analyzing recommender systems. - [LightFM](https://github.com/lyst/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](https://github.com/maciejkula/spotlight) Spotlight uses PyTorch to build both deep and shallow recommender models. - [Python-Recsys](https://github.com/ocelma/python-recsys) A python library for implementing a recommender system. - [LibRec](https://www.librec.net/) A java library for the state-of-the-art algorithms in recommeder sytem. - [SparkMovieLens](https://github.com/jadianes/spark-movie-lens) A scalable on-line movie recommender using Spark and Flask. - [Elasticsearch](https://github.com/IBM/elasticsearch-spark-recommender) Building a Recommender with Apache Spark & Elasticsearch. **Here are some categories which I think is very interesting**: | Topic | Papers | |:-: |:- | | Cold Start |- [RaPare: A Generic Strategy for Cold-Start Rating Prediction Problem](https://dl.acm.org/citation.cfm?doid=3108148)
- [Local Representative-Based Matrix Factorization for Cold-Start Recommendation ](https://dl.acm.org/citation.cfm?doid=3108148)
- [Low-Rank Linear Cold-Start Recommendation from Social Data](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14828)
- [A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14676)
- [Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors](http://aclweb.org/anthology/P17-1034)
- [A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness](https://www.ijcai.org/proceedings/2017/382)
- [Two Birds One Stone: On both Cold-Start and Long-Tail Recommendation ](https://dl.acm.org/citation.cfm?doid=3123266.3123316)
- [A Meta-Learning Perspective on Cold-Start Recommendations for Items ](http://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items)
- [DropoutNet: Addressing Cold Start in Recommender Systems ](http://papers.nips.cc/paper/7081-dropoutnet-addressing-cold-start-in-recommender-systems)
- [Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations](https://dl.acm.org/citation.cfm?doid=3109859.3109880)
- [On Learning Mixed Community-specific Similarity Metrics for Cold-start Link Prediction](https://dl.acm.org/citation.cfm?doid=3041021.3054269) | | Deep learning
(01) | - [Neural Attentional Rating Regression with Review-level Explanations](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf)
- [PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems ](http://proceedings.mlr.press/v77/liu17a/liu17a.pdf)
- [Boosting Recommender Systems with Deep Learning ](https://dl.acm.org/citation.cfm?doid=3109859.3109926)
- [Deep Learning for Recommender Systems ](https://dl.acm.org/citation.cfm?doid=3109859.3109933)
- [TransNets: Learning to Transform for Recommendation by Rose Catherine ](https://arxiv.org/abs/1704.02298)
- [Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation](https://arxiv.org/abs/1705.00105)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. ](https://arxiv.org/abs/1703.04247)
- [Deep Matrix Factorization Models for Recommender Systems](http://static.ijcai.org/proceedings-2017/0447.pdf)
- [Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network](https://www.ijcai.org/proceedings/2017/478)
- [Cross-Domain Recommendation: An Embedding and Mapping Approach](https://www.ijcai.org/proceedings/2017/0343.pdf)
- [Tag-Aware Personalized Recommendation Using a Hybrid Deep Model](https://www.ijcai.org/proceedings/2017/0446.pdf)
- [Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems ](https://dl.acm.org/citation.cfm?doid=3041021.3054227)| | Deep learning
(02) | - [What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation](http://www.public.asu.edu/~swang187/publications/VPOI.pdf)
- [Neural Collaborative Filtering](https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf)
- [Joint deep modeling of users and items using reviews for recommendation by L Zheng ](https://arxiv.org/pdf/1701.04783)
- [Collaborative Variational Autoencoder for Recommender Systems](https://dl.acm.org/citation.cfm?doid=3097983.3098077)
- [Dynamic Attention Deep Model for Article Recommendation](http://202.120.0.1/cache/6/03/wnzhang.net/3ac0c97001289a82d146e2d46405fc96/dadm-kdd.pdf)
- [A Hybrid Framework for Text Modeling with Convolutional RNN](https://dl.acm.org/citation.cfm?id=3098140)
- [Deep Embedding Forest: Forest-based Serving with Deep Embedding Features](https://dl.acm.org/citation.cfm?id=3098059&CFID=1018338121&CFTOKEN=87222562)
- [Embedding-based News Recommendation for Millions of Users](http://delivery.acm.org/10.1145/3100000/3098108/p1933-okura.pdf?ip=202.120.19.118&id=3098108&acc=OPENTOC&key=BF85BBA5741FDC6E%2E17676C47DFB149BF%2E4D4702B0C3E38B35%2E054E54E275136550&CFID=1018338121&CFTOKEN=87222562&__acm__=1513765592_2c508b428144fb4f0a1e885b3d20b9c8)
- [Deep Learning for Extreme Multi-label Text Classification](https://dl.acm.org/citation.cfm?id=3080834)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/citation.cfm?id=3080777&CFID=1018338121&CFTOKEN=87222562)
- [Neural Rating Regression with Abstractive Tips Generation for Recommendation](https://dl.acm.org/citation.cfm?id=3080822)
- [Multimedia Recommendation with Item- and Component-Level Attention](https://www.comp.nus.edu.sg/~xiangnan/papers/sigir17-AttentiveCF.pdf)| | Deep learning
(03) |- [Convolutional Matrix Factorization for Document Context-Aware Recommendation](http://dm.postech.ac.kr/~cartopy/ConvMF/)
- [Ask the GRU: Multi-task Learning for Deep Text Recommendations by T Bansal ](https://arxiv.org/pdf/1609.02116.pdf)
- [Deep Neural Networks for YouTube Recommendations by Paul Covington ](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf)
- [Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation ](https://arxiv.org/pdf/1607.07326.pdf)
- [A Neural Autoregressive Approach to Collaborative Filtering ](http://proceedings.mlr.press/v48/zheng16.pdf)
- [Collaborative Recurrent Neural Networks for Dynamic Recommender Systems](http://proceedings.mlr.press/v63/ko101.pdf)
- [Hybrid Recommender System based on Autoencoders](https://arxiv.org/pdf/1606.07659.pdf)
- [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems ](http://alicezheng.org/papers/wsdm16-cdae.pdf)
- [Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng ](https://arxiv.org/abs/1606.07792)
- [A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng ](http://bdsc.lab.uic.edu/docs/survey-critique-deep.pdf)
- [Collaborative Filtering with Recurrent Neural Networks by Robin Devooght ](https://arxiv.org/pdf/1608.07400.pdf)
- [Hybrid Collaborative Filtering with Neural Networks by Strub](https://pdfs.semanticscholar.org/fcbd/179590c30127cafbd00fd7087b47818406bc.pdf)
- [Learning Distributed Representations from Reviews for Collaborative Filtering ](http://dl.acm.org/citation.cfm?id=2800192)
- [A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](http://sonyis.me/paperpdf/frp1159-songA-www-2015.pdf)
- [Deep collaborative filtering via marginalized denoising auto-encoder by S Li ](https://pdfs.semanticscholar.org/ff29/2f00055d8221c42d4831679db9d3872b6fbd.pdf)
- [Deep content-based music recommendation by Aaron van den Oord ](https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf)
- [Restricted Boltzmann Machines for Collaborative Filtering by Ruslan Salakhutdinov](http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf) |