Attention augmented convolutional networks
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 …
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 …
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
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 …
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
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 …
explosive growth in recent years by providing customers with daily-life convenience. An …
Recsim: A configurable simulation platform for recommender systems
We propose RecSim, a configurable platform for authoring simulation environments for
recommender systems (RSs) that naturally supports sequential interaction with users …
recommender systems (RSs) that naturally supports sequential interaction with users …
Multi-factor sequential re-ranking with perception-aware diversification
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 …
and interact with, have gained significant popularity in practical applications. In feed …
Personalized re-ranking for recommendation
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 …
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
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 …
the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal …
Learning groupwise multivariate scoring functions using deep neural networks
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 …
individual document, in a ranking setting we determine the relevance ordering of the entire …
Coordinating CAV swarms at intersections with a deep learning model
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 …
safety and efficiency of traffic. One revolutionary CAV's impact on transportation system is …