Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

The textile and apparel industries have grown tremendously over the past few years.
Customers no longer have to visit many stores, stand in long queues, or try on garments in …

Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is
attributed to their capability on learning good user and item embeddings by exploiting the …

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …

Personalized recommendation plays a central role in many online content sharing platforms.
To provide quality micro-video recommendation service, it is of crucial importance to …

The security problems of smart contracts have drawn extensive attention due to the
enormous financial losses caused by vulnerabilities. Existing methods on smart contract …

Recommender systems are embracing conversational technologies to obtain user
preferences dynamically, and to overcome inherent limitations of their static models. A …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Nowadays, social media has become a tremendous source of acquiring user's opinions.
With the advancement of technology and sophistication of the internet, a huge amount of …

The ubiquity of implicit feedback makes them the default choice to build online
recommender systems. While the large volume of implicit feedback alleviates the data …