papers / frameworks / libraries
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A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Link 2 ) - WWW2015
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Collaborative Deep Learning for Recommender Systems Link 2 - KDD2015
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Deep Collaborative Filtering via Marginalized Denoising Auto-encoder Link 2 - CIKM2015
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Session-base Recommendations with Recurrent Neural Networks - ICLR2016
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Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation - RecSys2016
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Convolutional Matrix Factorization for Document Context-Aware Recommendation - RecSys2016
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Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations - RecSys2016
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Conversational Recommendation System with Unsupervised Learning - RecSys2016
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Ask the GRU: Multi-task Learning for Deep Text Recommendations ( Link 2 ) - RecSys2016
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Deep Auto-Encoding for Context-Aware Inference of Preferred Items' Categories RecSys 2016 Poster
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Improved Recurrent Neural Networks for Session-based Recommendations - DLRS2016
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Neural Autoregressive Collaborative Filtering for Implicit Feedback - DLRS2016
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Wide & Deep Learning for Recommender Systems Link 2 - DLRS2016
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Hybrid Recommender System based on Autoencoders Link 2 - DLRS2016
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Infusing Collaborative Recommenders with Distributed Representations Link 2 - DLRS2016
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Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation Link 2 - DLRS2016
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Collaborative Denoising Auto-Encoders for Top-N Recommender Systems Link 2 - WSDM2016
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Collaborative Knowledge Base Embedding for Recommender Systems Link 2 - KDD2016
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Multi-Rate Deep Learning for Temporal Recommendation Link 2 - SIGIR2016
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Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback Link 2 - PAKDD2016
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Recurrent Latent Variable Networks for Session-Based Recommendation - DLRS2017
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Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks - DLRS2017
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Inter-Session Modeling for Session-Based Recommendation - DLRS2017
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Specializing Joint Representations for the task of Product Recommendation - DLRS2017
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AutoSVD++: An E�icient Hybrid Collaborative Filtering Model via Contractive Auto-encoders Link 2 - SIGIR2017
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Neural Collaborative Filtering - WWW2017
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Localy Connected Deep Learning Framework for Industrial-scale Recommender Systems - WWW2017 Companion
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Recurrent Recommender Networks - WSDM2017
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Neural Survival Recommender - WSDM2017
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Joint Deep Modeling of Users and Items Using Reviews for Recommendation - WSDM2017
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Deep Coevolutionary Network: Embedding User and Item Features for Recommendation KDD2017
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Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration Link 2 - KDD2017
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Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation Link 2 - KDD2017
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TransNets: Learning to Transform for Recommendation - RecSys2017
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Sequential User-based Recurrent Neural Network Recommendations - RecSys2017
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Collaborative filtering and deep learning based recommendation system for cold start items Link 2 - Exp.Sys.App, 2017
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Deep hybrid recommender systems via exploiting document context and statistics of items - Inf.Sci, 2017
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Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network - MLRec2017 (In conjunction with SDM2017)
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Understanding Consumer Behavior with Recurrent Neural Networks - MLRec2017 (In conjunction with SDM2017)
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Relational Deep Learning: A Deep Latent Variable Model for Link Prediction - AAAI2017
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A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks - UMAP2017
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Trust-aware Collaborative Denoising Auto-Encoder for Top-N Recommendation - Arxiv 2017
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Deep Learning based Recommender System: A Survey and New Perspectives - Arxiv 2017
- Natural-Parameter Networks: A Class of Probabilistic Neural Networks - NIPS2016
- Towards Bayesian Deep Learning: A Framework and Some Existing Methods Link 2 - IEEE TKDE, 2016
- On Sampling Strategies for Neural Network-based Collaborative Filtering - KDD2017
- Generative Temporal Models with Memory - Arxiv, 2017 (Deepmind)