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Matchmaking is a core task in e-sports and online games, as it contributes to player engagement and further influences the game's lifecycle. Previous methods focus on creating fair games at all times. They divide players into different tiers based on ...

Self-attention models have achieved the state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user–item interactions. However, they rely on adding positional embeddings to the item sequence to retain ...

Event cameras are bio-inspired vision sensors with a high dynamic range (140 dB for event cameras <italic>vs.</italic> 60 dB for traditional cameras) and can be used to tackle the image degradation problem under extremely low-illumination scenarios, which ...

Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large ...

Federated learning is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data. As an important variant, vertical federated learning (VFL) deals with cases in which collaborating ...

• We provide an in-depth analysis of three strategies for stubborn mining and describe it as a Markov decision process. The optimal policy can be obtained by ...

Bitcoin uses a decentralized network of miners and a distributed consensus algorithm to agree on blockchains to process transactions, and designs certain incentive strategy to ensure the system run persistently. However, recent ...

Sequential recommender systems aim to predict users’ next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users’ long/short-term interests, which may be heterogeneous and contribute ...

Combinatorial optimization problems such as Traveling Salesman Problem (TSP) have a wide range of real-world applications in transportation, logistics, manufacturing. It has always been a difficult problem to solve large-scale TSP problems ...

Online games make up the largest segment of the booming global game market in terms of revenue as well as players. Unlike games that sell games at one time for profit, online games make money from in-game purchases by a large number of engaged players. ...

Selfish mining is notorious for receiving additional rewards disproportionate to the attacker’s mining power in Proof-of-Work (PoW) consensus-based blockchain, e.g., Bitcoin. Unfair reward distribution may cause partial honest miners to quit ...

Generally speaking, machine learning is to train an ML model (original model) on a dataset to perform a certain function. But sometimes, in order to protect the data privacy of a specified user, machine unlearning requires the original model owner ...${}^{\mathrm{}}$

Machine unlearning is the process through which a deployed machine learning model is enforced to forget about some of its training data items. It normally generates two machine learning models, the original model and the unlearned model, ...

Payment channel networks (PCNs) are generally regarded as one of the most effective and promising scalability solutions for blockchain‐based cryptocurrency systems, but suffer the issues of low success ratio and long confirmation latency in ...

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph ...

In the business domain,bundling is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual ...

In recent years, advances in Graph Convolutional Networks (GCNs) have given new insights into the development of social recommendation. However, many existing GCN-based social recommendation methods often directly apply GCN to capture user-item and user-...

Recent applications pose requirements of both cross-domain knowledge transfer and model compression to machine learning models due to insufficient training data and limited computational resources. In this paper, we propose a new knowledge ...

In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction ...

It is still challenging to continuously observe the marine ship motion in a harsh environmental condition. Combined science and technology assistance makes human maritime activities undergo a revolution with increasing artificial intelligence aboard ...

In recent years, the water transport industry is developing rapidly due to world trade prosperity, so that intricate waterways have become more frequent, and the number of ships is growing upwards. So, it is being challenged and very necessary to ...