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 are the main conferences within recommender system:
- 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.
- WSDM (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 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.
Here are some categories which I think is very interesting: