RecommenderSystem-Paper

这个仓库包含了一些我读过或认为可能非常有趣的论文。

大小193.9 MB
更新2019年11月21日

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daicoolb

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项目介绍

daicoolb/RecommenderSystem-Paper 是一个托管在 GitHub 上的开源项目，目前获得 744 个 Star、204 次 Fork。

仓库全名: daicoolb/RecommenderSystem-Paper
Star 数: 744
Fork 数: 204
未关闭 Issue: 1

README 摘要

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,…

完整内容见 GitHub 原始 README 或本页。

常见问题

如何获取 RecommenderSystem-Paper 的源码？

可以直接克隆仓库：git clone https://github.com/daicoolb/RecommenderSystem-Paper.git；安装与运行步骤见仓库 README。

RecommenderSystem-Paper 最近还在维护吗？

仓库最近一次更新为 2019年11月21日，当前有 1 个未关闭 Issue；Star 数为 744，可作为社区活跃度的参考。

本页面展示项目元数据与原始 README，GitRead 中文社区编辑团队负责选题收录。

关于

GitHub 原始描述（英文）

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

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

1896

价值分

581

活跃状态

活跃

cold-start deeplearning paper recommendation

GitHub

贡献者

daicoolb

bssbbsmd

README

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:

Topic	Papers
Cold Start	- RaPare: A Generic Strategy for Cold-Start Rating Prediction Problem - Local Representative-Based Matrix Factorization for Cold-Start Recommendation - Low-Rank Linear Cold-Start Recommendation from Social Data - A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems - Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors - A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness - Two Birds One Stone: On both Cold-Start and Long-Tail Recommendation - A Meta-Learning Perspective on Cold-Start Recommendations for Items - DropoutNet: Addressing Cold Start in Recommender Systems - Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations - On Learning Mixed Community-specific Similarity Metrics for Cold-start Link Prediction
Deep learning (01)	- Neural Attentional Rating Regression with Review-level Explanations - PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems - Boosting Recommender Systems with Deep Learning - Deep Learning for Recommender Systems - TransNets: Learning to Transform for Recommendation by Rose Catherine - Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation - DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. - 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 - Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems
Deep learning (02)	- What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation - Neural Collaborative Filtering - Joint deep modeling of users and items using reviews for recommendation by L Zheng - Collaborative Variational Autoencoder for Recommender Systems - Dynamic Attention Deep Model for Article Recommendation - 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 - Deep Learning for Extreme Multi-label Text Classification - Neural Factorization Machines for Sparse Predictive Analytics - Neural Rating Regression with Abstractive Tips Generation for Recommendation - Multimedia Recommendation with Item- and Component-Level Attention
Deep learning (03)	- Convolutional Matrix Factorization for Document Context-Aware Recommendation - 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 - A Neural Autoregressive Approach to Collaborative Filtering - Collaborative Recurrent Neural Networks for Dynamic Recommender Systems - Hybrid Recommender System based on Autoencoders - Collaborative Denoising Auto-Encoders for Top-N Recommender Systems - Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng - A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng - Collaborative Filtering with Recurrent Neural Networks by Robin Devooght - Hybrid Collaborative Filtering with Neural Networks by Strub - Learning Distributed Representations from Reviews for Collaborative Filtering - A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems - Deep collaborative filtering via marginalized denoising auto-encoder by S Li - Deep content-based music recommendation by Aaron van den Oord - Restricted Boltzmann Machines for Collaborative Filtering by Ruslan Salakhutdinov

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