Attention augmented convolutional networks

I Bello, B Zoph, A Vaswani… - Proceedings of the …, 2019 - openaccess.thecvf.com
Convolutional networks have enjoyed much success in many computer vision applications.
The convolution operation however has a significant weakness in that it only operates on a …

Fairness in recommendation ranking through pairwise comparisons

A Beutel, J Chen, T Doshi, H Qian, L Wei… - Proceedings of the 25th …, 2019 - dl.acm.org
Recommender systems are one of the most pervasive applications of machine learning in
industry, with many services using them to match users to products or information. As such it …

UAV trajectory planning in wireless sensor networks for energy consumption minimization by deep reinforcement learning

B Zhu, E Bedeer, HH Nguyen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) have emerged as a promising candidate solution for data
collection of large-scale wireless sensor networks (WSNs). In this paper, we investigate a …

A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects

H Wen, Y Lin, L Wu, X Mao, T Cai, Y Hou… - … on Knowledge and …, 2024 - ieeexplore.ieee.org
Instant delivery services, such as food delivery and package delivery, have achieved
explosive growth in recent years by providing customers with daily-life convenience. An …

Recsim: A configurable simulation platform for recommender systems

E Ie, C Hsu, M Mladenov, V Jain, S Narvekar… - arXiv preprint arXiv …, 2019 - arxiv.org
We propose RecSim, a configurable platform for authoring simulation environments for
recommender systems (RSs) that naturally supports sequential interaction with users …

Multi-factor sequential re-ranking with perception-aware diversification

Y Xu, H Chen, Z Wang, J Yin, Q Shen, D Wang… - Proceedings of the 29th …, 2023 - dl.acm.org
Feed recommendation systems, which recommend a sequence of items for users to browse
and interact with, have gained significant popularity in practical applications. In feed …

Personalized re-ranking for recommendation

C Pei, Y Zhang, Y Zhang, F Sun, X Lin, H Sun… - Proceedings of the 13th …, 2019 - dl.acm.org
Ranking is a core task in recommender systems, which aims at providing an ordered list of
items to users. Typically, a ranking function is learned from the labeled dataset to optimize …

Setrank: Learning a permutation-invariant ranking model for information retrieval

L Pang, J Xu, Q Ai, Y Lan, X Cheng, J Wen - Proceedings of the 43rd …, 2020 - dl.acm.org
In learning-to-rank for information retrieval, a ranking model is automatically learned from
the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal …

Learning groupwise multivariate scoring functions using deep neural networks

Q Ai, X Wang, S Bruch, N Golbandi… - Proceedings of the …, 2019 - dl.acm.org
While in a classification or a regression setting a label or a value is assigned to each
individual document, in a ranking setting we determine the relevance ordering of the entire …

Coordinating CAV swarms at intersections with a deep learning model

J Zhang, S Li, L Li - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
Connected and automated vehicles (CAVs) have the potential to significantly improve the
safety and efficiency of traffic. One revolutionary CAV's impact on transportation system is …