With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …

Several recent works have considered the problem of generating reviews (or 'tips') as a form
of explanation as to why a recommendation might match a customer's interests. While …

Recommendation systems have witnessed significant advancements and have been widely
used over the past decades. However, most traditional recommendation methods are task …

Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short sessions …

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

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Industrial recommender systems have been growing increasingly complex, may
involve\emph {diverse domains} such as e-commerce products and user-generated …

Providing user-understandable explanations to justify recommendations could help users
better understand the recommended items, increase the system's ease of use, and gain …