With the rapid growth of information, recommender systems have become
in...
Conversational recommender systems (CRSs) aim to recommend high-quality ...
Weight-sharing supernet has become a vital component for performance
est...
The generalization of neural networks is a central challenge in machine
...
The development of knowledge graph (KG) applications has led to a rising...
Machine unlearning aims to erase the impact of specific training samples...
Self-attention based transformer models have been dominating many comput...
Learned recommender systems may inadvertently leak information about the...
Recently, privacy issues in web services that rely on users' personal da...
Weight pruning in deep neural networks (DNNs) can reduce storage and
com...
Recommender systems provide essential web services by learning users'
pe...
Deep neural networks (DNNs) have shown to provide superb performance in ...
Deep neural networks (DNNs) have been proven to be effective in solving ...
Despite significant progress has been achieved in text summarization, fa...
To improve user experience and profits of corporations, modern industria...
Conversational recommender systems (CRS) enable the traditional recommen...
Reranking is attracting incremental attention in the recommender systems...
Multi-stage ranking pipelines have been a practical solution in modern s...
Recommender systems play a vital role in modern online services, such as...
Recommender systems (RS) work effective at alleviating information overl...
We present Graph Attention Collaborative Similarity Embedding (GACSE), a...
Pre-trained language models have proven their unique powers in capturing...
With the explosive growth of online information, recommender systems pla...
Sequential recommendation methods play a crucial role in modern recommen...
Deep learning-based sequential recommender systems have recently attract...
Learning informative representations (aka. embeddings) of users and item...
Click-through rate (CTR) prediction is a critical task for many industri...
How to effectively utilize the dialogue history is a crucial problem in
...
The rapid growth of e-commerce has made people accustomed to shopping on...
Personalized recommendation benefits users in accessing contents of inte...
Neural network models are widely used in solving many challenging proble...
To address the large model size and intensive computation requirement of...
Recently, with the availability of cost-effective depth cameras coupled ...
Deep neural networks have achieved remarkable success in computer vision...
Machine-learning (ML) hardware and software system demand is burgeoning....
Capturing users' precise preferences is a fundamental problem in large-s...
Recent research has made impressive progress in single-turn dialogue
mod...
This paper targets to a novel but practical recommendation problem named...
Click-Through Rate (CTR) prediction plays an important role in many
indu...
Increasing demand for fashion recommendation raises a lot of challenges ...
Network embedding has proved extremely useful in a variety of network
an...
Computer vision has achieved impressive progress in recent years. Meanwh...
Ranking is a core task in E-commerce recommender systems, which aims at
...
Modeling users' dynamic and evolving preferences from their historical
b...
Existing recommendation algorithms mostly focus on optimizing traditiona...
This paper proposes an efficient neural network (NN) architecture design...
Designing accurate and efficient ConvNets for mobile devices is challeng...
The Low-Power Image Recognition Challenge (LPIRC,
https://rebootingcompu...
In this paper, we study the product title summarization problem in E-com...
Recent work exhibited that distributed word representations are good at
...