The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

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

Large language models (LLMs) have demonstrated their significant potential to be applied
for addressing various application tasks. However, traditional recommender systems …

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 …

The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering
personalized recommender systems to incorporate various modalities (eg, visual, textual …

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

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

By providing explanations for users and system designers to facilitate better understanding
and decision making, explainable recommendation has been an important research …

Recent years have witnessed the wide adoption of large language models (LLM) in different
fields, especially natural language processing and computer vision. Such a trend can also …

Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …