Omprakash Sonie
ABSTRACT
Deep Learning has shown significant results in Computer Vision, Natural Language Processing, Speech and recommender systems. Promising techniques include Embedding, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variant Long Short-Term Memory (LSTM and Bi-directional LSTMs), Attention, Autoencoders, Generative Adversarial Networks (GAN) and Bidirectional Encoder Representations from Transformer (BERT).
Multi-task learning (MTL) has led to successes in many applications of machine learning. We are proposing a tutorial for applying MTL for recommendation, improving recommendation and providing explanation. We cover few recent and diverse techniques which will be used for hands-on session.
We believe that a self-contained tutorial giving good conceptual understanding of MTL technique with sufficient mathematical background along with actual code will be of immense help to RecSys participants.
- Sebastian Ruder. 2017 An Overview of Multi-Task Learning in Deep Neural Networks.Google Scholar
- Jia Dong Zhang et al. SEMAX: Multi-Task Learning for Improving Recommendations IEEE-2018Google Scholar
- Yichao Lu at el. Why I like it: Multi-task Learning for Recommendation and Explanation. RecSys-2018 Google ScholarDigital Library
- Chen Gao et al. Neural Multi-Task Recommendation from Multi-Behavior Data. ICDE-2017Google Scholar
- Qiaolin Xia1 et al. Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning. CIKM-2018 Google ScholarDigital Library
- Nan Wang et al. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. SIGIR-2018 Google ScholarDigital Library
- Jizhou Huang et al. Improving Entity Recommendation with Search Log and Multi-Task Learning IJCAI-2018 Google ScholarDigital Library
- Yujie Lin et al,. Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation. 2018Google Scholar
- Yujie Lin et al. Improving Outfit Recommendation with Co-supervision of Fashion Generation. WWW-2019 Google ScholarDigital Library
- Weizhi Ma et al. Jointly Learning Explainable Rules for Recommendation with Knowledge Graph. WWW-2019 Google ScholarDigital Library
- Hongwei Wang. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW-2019 Google ScholarDigital Library
- Fei Sun, et. al. BERT4Rec- Sequential Recommendation with Bidirectional Encoder Representations from Transformer. WOODSTOCK 2019Google Scholar
Index Terms
- Concept to code: deep learning for multitask recommendation
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