Papers: Deep learning for recommender system

Mar 13, 2017


Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume, complexity and dynamicity of online information, recommender system is an effective key solution to overcome such information overload. In recent years, deep learning’s revolutionary advances in speech recognition, image analysis and natural language processing have drawn significant attention. Meanwhile, recent studies also demonstrate its effectiveness in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performances and high-quality recommendations. In contrast to traditional recommendation models, deep learning provides a better understanding of user’s demands, item’s characteristics and historical interactions between them. This article provides a comprehensive review of recent research efforts on deep learning based recommender systems towards fostering innovations of recommender system research. A taxonomy of deep learning based recommendation models is presented and used to categorise surveyed articles. Open problems are identified based on the insightful analytics of the reviewed works and potential solutions discussed.

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  • Restricted Boltzmann machines for collaborative filtering
  • Deep content-based music recommendation
  • Collaborative Variational Autoencoder for Recommender Systems
  • DLTSR: A Deep Learning Framework for Recommendation of Long-tail Web Services
  • Online news recommender based on stacked auto-encoder
  • Representation Learning with Pair-wise Constraints for Collaborative Ranking
  • Trust-aware Top-N Recommender Systems with Correlative Denoising Autoencoder
  • Autoencoder-Based Collaborative Filtering
  • Expanded autoencoder recommendation framework and its application in movie recommendation, multitask
  • Representation learning via Dual-Autoencoder for recommendation
  • Stacked Denoising Autoencoder-Based Deep Collaborative Filtering Using the Change of Similarity
  • A hybrid recommendation system considering visual information for predicting favorite restaurants
  • DeepStyle: Learning User Preferences for Visual Recommendation
  • Personalized Deep Learning for Tag Recommendation
  • Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network
  • Deep Hybrid Recommender Systems via Exploiting Document Context and Statistics of Items, same with above
  • Modeling Interestingness with Deep Neural Networks
  • Location-Aware News Recommendation Using Deep Localized Semantic Analysis
  • IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
  • Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network
  • Quote Recommendation in Dialogue using Deep Neural Network
  • Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors Demonstration
  • Neural Citation Network for Context-Aware Citation Recommendation
  • Neural Rating Regression with Abstractive Tips Generation for Recommendation
  • Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level A‚ention
  • Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI recommendation
  • DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
  • Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems
  • Neural Semantic Personalized Ranking for item cold-start recommendation
  • CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems
  • User Occupation Aware Conditional Restricted Boltzmann Machine Based Recommendation
  • Hashtag recommendation with topical attention-based LSTM
  • Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks
  • Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
  • Deep Sequential Recommendation for Personalized Adaptive User Interfaces
  • Embedding-based News Recommendation for Millions of Users
  • Deep Coevolutionary Network: Embedding User and Item Features for Recommendation
  • A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks
  • JOINT TRAINING OF RATINGS AND REVIEWS WITH RECURRENT RECOMMENDER NETWORKS
  • Item Category Aware Conditional Restricted Boltzmann Machine Based Recommendation
  • Neural Autoregressive Collaborative Filtering for Implicit Feedback
  • Applying Visual User Interest Profiles for Recommendation and Personalisation
  • Visual Background Recommendation for Dance Performances Using Dancer-Shared Images
  • ConTagNet: Exploiting User Context for Image Tag Recommendation
  • Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
  • Collaborative restricted Boltzmann machine for social event recommendation
  • A Neural Network Approach to Quote Recommendation in writings
  • Neural Emoji Recommendation in Dialogue Systems
  • Recurrent neural network based recommendation for time heterogeneous feedback
  • Multi-modal learning for video recommendation based on mobile application usage
  • A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems
  • AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
  • Hashtag recommendation using attention-based convolutional neural network
  • Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network
  • Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling
  • Examples-Rules Guided Deep Neural Network for Makeup Recommendation
  • Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks
  • Personal recommendation using deep recurrent neural networks in NetEase
  • Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation
  • Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem
  • On Deep Learning for Trust-Aware Recommendations in Social Networks
  • Collaborative recurrent autoencoder: recommend while learning to fill in the blanks
  • Comparative Deep Learning of Hybrid Representations for Image Recommendations
  • Latent Context-Aware Recommender Systems
  • Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback
  • Item Silk Road: Recommending Items from Information Domains to Social Users
  • Collaborative Recurrent Neural Networks for Dynamic Recommender Systems
  • Neural Survival Recommender
  • CONTENT-AWARE COLLABORATIVE MUSIC RECOMMENDATION USING PRE-TRAINED NEURAL NETWORKS
  • Multi-Rate Deep Learning for Temporal Recommendation
  • Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations
  • Tag-aware recommender systems based on deep neural networks
  • Towards latent context-aware recommendation systems
  • Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs
  • Hybrid Recommender System based on Autoencoders
  • A Neural autoregressive approach to collaborative filtering
  • Ask the GRU: Multi-task Learning for Deep Text Recommendations
  • What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation
  • Convolutional Matrix Factorization for Document Context-Aware Recommendation
  • Improved Recurrent Neural Networks for Session-based Recommendations
  • A Neural Probabilistic Model for Context Based Citation Recommendation
  • Joint Deep Modeling of Users and Items Using Reviews for Recommendation
  • Collaborative Knowledge Base Embedding for Recommender Systems
  • A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines
  • Relational stacked denoising autoencoder for tag recommendation
  • Collaborative Filtering and Deep Learning Based Recommendation System For Cold Start Items
  • Recurrent Recommender Networks
  • Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
  • Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering, cold start
  • Neural Collaborative Filtering
  • VBPR: visual bayesian personalized ranking from implicit feedback
  • Wide & Deep Learning for Recommender Systems
  • AutoRec: Autoencoders Meet Collaborative Filtering
  • Improving Content-based and Hybrid Music Recommendation using Deep Learning
  • Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
  • A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
  • Session-based Recommendations with Recurrent Neural Networks
  • Deep Neural Networks for YouTube Recommendations
  • Image-based Recommendations on Styles and Substitutes
  • Collaborative Deep Learning for Recommender Systems
  • Neural Factorization Machines for Sparse Predictive Analytics
  • Collaborative Filtering with Recurrent Neural Networks

New

  • Tag-Aware Personalized Recommendation Using a Hybrid Deep Model, IJCAI 2017
  • Deep Matrix Factorization Models for Recommender Systems, IJCAI 2017
  • A Deep Multimodal Approach for Cold-start Music Recommendation, DLRS 2017
  • Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks, DLRS 2017
  • Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation, DLRS 2017
  • Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios, DLRS 2017
  • Inter-Session Modeling for Session-Based Recommendation, DLRS 2017
  • Towards Recommender Systems for Police Photo Lineup, DLRS 2017
  • Specializing Joint Representations for the task of Product Recommendation, DLRS 2017
  • Recurrent Latent Variable Networks for Session-Based Recommendation, DLRS 2017
  • Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set Songs, DLRS 2017
  • Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems
  • On Sampling Strategies for Neural Network-based Collaborative Filtering
  • 3D Convolutional Networks for Session-based Recommendation with Content Features
  • Deep Neural Networks for News Recommendations