Concept to code: deep learning for multitask recommendation
Pages 584 - 585
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.
References
[1]
Sebastian Ruder. 2017 An Overview of Multi-Task Learning in Deep Neural Networks.
[2]
Jia Dong Zhang et al. SEMAX: Multi-Task Learning for Improving Recommendations IEEE-2018
[3]
Yichao Lu at el. Why I like it: Multi-task Learning for Recommendation and Explanation. RecSys-2018
[4]
Chen Gao et al. Neural Multi-Task Recommendation from Multi-Behavior Data. ICDE-2017
[5]
Qiaolin Xia1 et al. Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning. CIKM-2018
[6]
Nan Wang et al. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. SIGIR-2018
[7]
Jizhou Huang et al. Improving Entity Recommendation with Search Log and Multi-Task Learning IJCAI-2018
[8]
Yujie Lin et al,. Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation. 2018
[9]
Yujie Lin et al. Improving Outfit Recommendation with Co-supervision of Fashion Generation. WWW-2019
[10]
Weizhi Ma et al. Jointly Learning Explainable Rules for Recommendation with Knowledge Graph. WWW-2019
[11]
Hongwei Wang. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW-2019
[12]
Fei Sun, et. al. BERT4Rec- Sequential Recommendation with Bidirectional Encoder Representations from Transformer. WOODSTOCK 2019
Index Terms
- Concept to code: deep learning for multitask recommendation
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Information & Contributors
Information
Published In
September 2019
635 pages
ISBN:9781450362436
DOI:10.1145/3298689
- General Chairs:
- Toine Bogers,
- Alan Said,
- Program Chairs:
- Peter Brusilovsky,
- Domonkos Tikk
Copyright © 2019 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 September 2019
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- Tutorial
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RecSys '19
RecSys '19: Thirteenth ACM Conference on Recommender Systems
September 16 - 20, 2019
Copenhagen, Denmark
Acceptance Rates
RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%
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