Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice, and the theoretical analysis of the proposed framework gives its connection with previous works and proving its better expressiveness.

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OTTO Recommender Systems Dataset

LSEC

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Retrieving Similar E-Commerce Images Using Deep Learning

Rishab Sharma, Anirudha Vishvakarma•Thu Jan 10 2019

A deep siamese architecture that when trained on positive and negative pairs of images learn an embedding that accurately approximates the ranking of images in order of visual similarity notion is presented.

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TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

Jiawei Han, Zhihong Shen, Kuansan Wang, Chenyan Xiong, Jiaming Shen•Sat Jan 25 2020

A novel self-supervised framework, named TaxoExpan, which automatically generates a set of ⟨query concept, anchor concept⟩ pairs from the existing taxonomy as training data, and develops two innovative techniques, including a position-enhanced graph neural network that encodes the local structure of an anchor concept in theexisting taxonomy and a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self- supervision data.

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A Tutorial on Thompson Sampling

Ian Osband, Benjamin Van Roy, Daniel Russo, Abbas Kazerouni•Thu Jul 06 2017

This tutorial covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and reinforcement learning in Markov decision processes.

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MILDNet: A Lightweight Single Scaled Deep Ranking Architecture

Anirudha Vishvakarma•Sat Mar 02 2019

This paper proposes a competing novel CNN architecture, called MILDNet, which merits by being vastly compact (about 3 times), and Inspired by the fact that successive CNN layers represent the image with increasing levels of abstraction, compressed the authors' deep ranking model to a single CNN by coupling activations from multiple intermediate layers along with the last layer.

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Distributed Learning of Deep Neural Networks using Independent Subnet Training

Binhang Yuan, Anastasios Kyrillidis, C. Jermaine•Thu Oct 03 2019

Independent subnet training is introduced: a simple, jointly model- parallel and data-parallel, approach to distributed training for fully connected, feed-forward neural networks, where it often results into boosting the testing accuracy, as it implicitly combines dropout and batch normalization regularizations during training.

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RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising

Alexandros Karatzoglou, D. Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile•Wed Aug 01 2018

RecoGym is introduced, an RL environment for recommendation, which is defined by a model of user traffic patterns on e-commerce and the users response to recommendations on the publisher websites, that could open up an avenue of collaboration between the recommender systems and reinforcement learning communities and lead to better alignment between offline and online performance metrics.

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Adding a benchmark result helps the community track progress.