In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems.
Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).
Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research.
Temporal action detection on unconstrained videos has seen significant research progress in recent years.
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e. g., in reinforcement learning based recommender systems.
The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model's inherent bias towards seen classes.
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.
A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.
Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.
Accurate demand forecasting of different public transport modes(e. g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies sig-nificantly, which makes it hard to predict the demand of the modeswith insufficient knowledge and sparse station distribution (i. e., station-sparse mode).
Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer.
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.
Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT).
However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users.
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #3 on Traffic Prediction on PeMS04
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.
In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS).
Point cloud is a principal data structure adopted for 3D geometric information encoding.
Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models.
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.
The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.
In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN).
In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection.
In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).
All in all, we conduct extensive experiments on six real-world datasets, demonstrating the effectiveness of Quaternion algebra in recommender systems.
Cross-domain recommendation has long been one of the major topics in recommender systems.
In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow.
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.
The resulting graph of prototypes can be continually re-used and updated for new tasks and classes.
However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance.
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings.
Ranked #5 on Link Prediction on FB15k
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
In this work, to address the above issue, we propose a general adversial training framework for neural network-based recommendation models, which improves both the model robustness and the overall performance.
We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance.
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR).
Online reviews play an important role in influencing buyers' daily purchase decisions.
Modeling user-item interaction patterns is an important task for personalized recommendations.
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Ranked #1 on Graph Clustering on Cora (F1 metric)
Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR).
The proposed approach is evaluated over 3 datasets (two local and one public).
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.
In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition.
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions.
Human-Computer Interaction Neurons and Cognition
This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems.
Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics.
In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.