lmcRS / AWS-recommendation-papers

AWS-recommendation-papers

This is the repository of the survey paper "A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation" submitted in IEEE Transactions on Knowledge and Data Engineering

Summarization of representation learning approaches for CF

Classical Matrix Factorization

• Bpr: Bayesian personalized ranking from implicit feedback, S. Rendle. UAI, 2009.
• Matrix factorization tech- niques for recommender systems,Y. Koren. Computer, 2009.
• FISM: factored item similarity models for top-n recommender systems,S. Kabbur. SIGKDD, 2013.
• Probabilistic metric learning with adaptive margin for top-k recommendation, C. Ma. SIGKDD, 2020.
• Product quantized collaborative filtering, D. Lian. IEEE TKDE, 2020.
• Fast adaptively weighted matrix factorization for recommendation with implicit feedback, J. Chen. AAAI, 2020.
• Factorization meets the neighborhood: a multifaceted collaborative filtering model, Y. Koren. SIGKDD, 2008.

History Attention

• Nais: Neural attentive item similarity model for recommendation, X. He. IEEE TKDE, 2018.
• Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention, J. Chen. SIGIR, 2017.

Autoencoder Models

• Autorec: Autoencoders meet collaborative filtering, S. Sedhain. WWW, 2015.
• Collaborative denoising auto-encoders for top-n recommender systems, Y. Wu. WSDM, 2016.
• Variational autoencoders for collaborative filtering, D. Liang. WWW, 2018.
• Representation learning with pair-wise constraints for collaborative ranking, F. Zhuang. WSDM, 2017.
• Deep critiquing for vae-based recommender systems, K. Luo. SIGIR, 2020.
• Learning the structure of auto-encoding recommenders,F. Khawar. WWW, 2020.

Graph Learning

• Graph convolutional matrix completion, R. van denBerg. ArXiv, vol. abs/1706.02263, 2017.
• Neural graph collaborative filtering, X. Wang. SIGIR, 2019.
• Spectral collaborative filtering, L. Zheng. RecSys, 2018.
• Neighbor interaction aware graph convolution networks for recommendation, J. Sun. SIGIR, 2020.
• A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks, J. Sun. SIGKDD, 2020.
• Disentangled graph collaborative filtering, X. Wang. SIGIR, 2020.
• Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach, L. Chen. AAAI, 2020.
• Lightgcn: Simplifying and powering graph convolution network for recommendation, X. He. SIGIR, 2020.
• Dual channel hypergraph collaborative filtering, S. Ji. SIGKDD, 2020.

Interaction modeling techniques

Distance Modeling

• Collaborative metric learning, C.-K. Hsieh. WWW, 2017.
• Translation-based recommendation, R. He. RecSys, 2017.
• Latent relational metric learning via memory-based attention for collaborative ranking, Y. Tay. WWW, 2018.
• Hyperml: A boosting metric learning approach in hyperbolic space for recommender systems, L. V. Tran. WSDM, 2020.

Neural Networks

• Neural collaborative filtering, X. He. WWW, 2017.
• Outer product-based neural collaborative filtering, X. He. IJCAI, 2018.
• Autorec: Autoencoders meet collaborative filtering, S. Sedhain. WWW, 2015.
• Collaborative denoising auto-encoders for top-n recommender systems, Y. Wu. WSDM, 2016.

Classification of modeling feature-enhanced CF

Second order

• Factorization machines, S. Rendle, ICDM, 2010.
• Field-aware factorization machines for ctr prediction, Y. Juan. RecSys, 2016.

MLP based higher order

• Neural factorization machines for sparse predictive analytics, X. He. SIGIR, 2017.
• Deep learning over multi-field categorical data, W. Zhang. ECIR, 2016.
• Product-based neural networks for user response prediction, Y. Qu. ICDM, 2016.
• Deep crossing: Web-scale modeling without manually crafted combinatorial features, Y. Shan. SIGKDD, 2016.
• Deep neural networks for youtube recommendations, P. Covington. RecSys, 2016.
• Wide & deep learning for recommender systems, H.-T. Cheng. DLRS, 2016.
• Deepfm: A factorization-machine based neural network for CTR prediction, H. Guo. IJCAI, 2017.

Up to K^{th} order modeling

• Deep & cross network for ad click predictions, R. Wang. ADKDD, 2017.
• xdeepfm: Combining explicit and implicit feature interactions for recommender systems, J. Lian. SIGKDD, 2018.

Tree structure

• Tem: Tree-enhanced embedding model for explainable recommendation, X. Wang. WWW, 2018.

Multimedia based recommendation models with different kinds of multimedia input

Image

• Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention, J. Chen. SIGIR, 2017.
• VBPR: visual bayesian personalized ranking from implicit feedback, R. He and J. McAuley. AAAI, 2016.
• Outfitnet: Fashion outfit recommendation with attention-based multiple instance learning, Y. Lin. WWW, 2020.
• Aesthetic-based clothing recommendation, W. Yu. WWW, 2018.
• Visual background recommendation for dance performances using dancer-shared images, J. Wen. iThings, 2016.
• Explainable fashion recommendation: A semantic attribute region guided approach, M. Hou. IJCAI, 2019.
• Examples-rules guided deep neural network for makeup recommendation, T. Alashkar. AAAI, 2017.
• Interpretable fashion matching with rich attributes, X. Yang. SIGIR, 2019.
• User-video co-attention network for personalized micro-video recommendation, S. Liu. WWW, 2019.
• Graph convolutional neural networks for web-scale recommender systems, R. Ying. SIGKDD, 2018.
• Hierarchical fashion graph network for personalized outfit recommendation, X. Li. SIGIR, 2020.
• Learning to transfer graph embeddings for inductive graph based recommendation, L. Wu. SIGIR, 2020.

Image + Behavior Time

• Aesthetic-based clothing recommendation, W. Yu. WWW, 2018.
• Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering, R. He. WWW, 2016.

Image+Text

• Visual background recommendation for dance performances using dancer-shared images, J. Wen. iThings, 2016.
• Graphcar: Content-aware multimedia recommendation with graph autoencoder, Q. Xu. SIGIR, 2018.
• Collaborative knowledge base embedding for recommender systems, F. Zhang. SIGKDD, 2016.
• Transnfcm: Translation-based neural fashion compatibility modeling, X. Yang. AAAI, 2019.
• Mention recommendation for multimodal microblog with cross-attention memory network, R. Ma. SIGIR, 2018.
• Hashtag recommendation for multimodal microblog using co-attention network, Q. Zhang. IJCAI, 2017.
• Product characterisation towards personalisation: Learning attributes from unstructured data to recommend fashion products, Â. Cardoso. SIGKDD, 2018.
• Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation, X. Chen. SIGIR, 2019.
• Personalized key frame recommendation, X. Chen. SIGIR, 2017.
• Multimodal review generation for recommender systems, Q.-T. Truong. WWW, 2019.
• Towards hands-free visual dialog interactive recommendation, T. Yu. AAAI, 2020.

Audio

• Improving content-based and hybrid music recommendation using deep learning, X. Wang. MM, 2014.
• Deep content-based music recommendation, A. v. d. Oord. NeurIPS, 2013.
• Recommending podcasts for cold-start users based on music listening and taste, Z. Nazari. SIGIR, 2020.

Video

• Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention, J. Chen. SIGIR, 2017.
• Personalized multimedia item and key frame recommendation, L. Wu. IJCAI, 2019.
• Joint item recommendation and attribute inference: An adaptive graph convolutional network approach, L. Wu. SIGIR, 2020.

Video+Audio

• Collaborative deep metric learning for video understanding, J. Lee. SIGKDD, 2018.
• Large-scale content-only video recommendation, J. Lee. ICCVW, 2017.

A comparison of methods that models the temporal and sequential effects in RS

Temporal Models

• Attentive recurrent social recommendation, P. Sun. SIGIR, 2018.
• Recurrent recommender networks, C.-Y. Wu. WSDM, 2017.
• Neural survival recommender, H. Jing. WSDM, 2017.
• Recurrent coevolutionary latent feature processes for continuous-time recommendation, H. Dai. DLRS, 2016.
• Deep modeling of the evolution of user preferences and item attributes in dynamic social networks, P. Wu. WWW, 2018.
• Latent cross: Making use of context in recurrent recommender systems, A. Beutel. WSDM, 2018.
• Sequential user-based recurrent neural network recommendations, T. Donkers. RecSys, 2017.
• Neural memory streaming recommender networks with adversarial training, Q. Wang. SIGKDD, 2018.
• Sequential recommendation with user memory networks, X. Chen. WSDM, 2018.
• STAMP: short-term attention/memory priority model for session-based recommendation, Q. Liu. SIGKDD, 2018.

Sequential Models

• Parallel recurrent neural network architectures for feature-rich session-based recommendations, B. Hidasi. RecSys, 2016.
• Kerl: A knowledge-guided reinforcement learning model for sequential recommendation, P. Wang. SIGIR, 2020.
• Contextual sequence modeling for recommendation with recurrent neural networks, E. Smirnova. DLRS, 2017.
• Neural attentive session-based recommendation, J. Li. CIKM, 2017.
• Improved recurrent neural networks for session-based recommendations, Y. K. Tan. DLRS, 2016.
• When recurrent neural networks meet the neighborhood for session-based recommendation, D. Jannach. RecSys, 2017.
• Modeling user session and intent with an attention-based encoder-decoder architecture, P. Loyola. RecSys, 2017.
• Session-based recommendations with recurrent neural networks, B. Hidasi. ICLR, 2016.
• Translation-based recommendationhttps://doi.org/10.1145/3109859.3109882, R. He. RecSys, 2017.
• Parameter-efficient transfer from sequential behaviors for user modeling and recommendation, F. Yuan. SIGIR, 2020.
• Learning transferrable parameters for long-tailed sequential user behavior modeling, J. Yin. SIGKDD, 2020.
• 3d convolutional networks for session-based recommendation with content features, T. X. Tuan. RecSys, 2017.
• Self-attentive sequential recommendation, W.-C. Kang. ICDM, 2018.
• Sequential recommendation with self-attentive multi-adversarial network, R. Ren. SIGIR, 2020.
• Modeling mobile user actions for purchase recommendation using deep memory networks, D. Gligorijevic. SIGIR, 2018.
• Session-based recommendation with graph neural networks, S. Wu. AAAI, 2019.
• Graph contextualized self-attention network for session-based recommendation, C. Xu. IJCAI, 2019.
• Gag: Global attributed graph neural network for streaming session-based recommendation, R. Qiu. SIGIR, 2020.
• Global context enhanced graph neural networks for session-based recommendation, Z. Wang. SIGIR, 2020.
• Star graph neural networks for session-based recommendation, Z. Pan. CIKM, 2020.
• Handling information loss of graph neural networks forsession-based recommendation, T. Chen. SIGKDD, 2020.

Temporal and Sequential Models

• Sequential recommender system based on hierarchical attention networks, H. Ying. IJCAI, 2018.
• Learning hierarchical representation model for nextbasket recommendation, P. Wang. SIGIR, 2015.
• Hierarchical gating networks for sequential recommendation, C. Ma. SIGKDD, 2019.
• Multi-order attentive ranking model for sequential recommendation, L. Yu. AAAI, 2019.
• Fissa:fusing item similarity models with self-attentionnetworks for sequential recommendation, J. Lin. RecSys, 2020.
• Sse-pt: Sequential recommendation via personalized transformer, L. Wu. RecSys, 2020.
• Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors, Z. Li. SIGKDD, 2018.
• Recurrent recommender networks, C.-Y. Wu. WSDM, 2017.
• Personalizing session-based recommendations with hierarchical recurrent neural networks, M. Quadrana. RecSys, 2017.
• Ctrec: A long-short demands evolution model for continuoustime recommendation, T. Bai. SIGIR, 2019.
• Towards neural mixture recommender for long range dependent user sequences, J. Tang. WWW, 2019.
• S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization, K. Zhou. CIKM, 2020.
• Déjà vu: A contextualized temporal attention mechanism for sequential recommendation, J. Wu. WWW, 2020.
• Sequential recommender via time-aware attentive memory network, W. Ji. CIKM, 2020.
• Time matters: Sequential recommendation with complex temporal information, W. Ye. SIGIR, 2020.
• Make it a chorus: knowledge-and time-aware item modeling for sequential recommendation, C. Wang. SIGIR, 2020.
• Sequential modeling of hierarchical user intention and preference for next-item recommendation, N. Zhu. WSDM, 2020.
• A collaborative session-based recommendation approach with parallel memory modules, M. Wang. SIGIR, 2019.
• Personalized top-n sequential recommendation via convolutional sequence embedding, J. Tang. WSDM, 2018.
• A simple convolutional generative network for next item recommendation, F. Yuan. WSDM, 2019.
• Next-item recommendation with sequential hypergraphs, J. Wang. SIGIR, 2020.
• Memory augmented graph neural networks for sequential recommendation, C. Ma. AAAI, 2020.
• Intention modeling from ordered and unordered facets for sequential recommendation, X. Guo. WWW, 2020.