Existing works in recommender system have widely explored extracting reviews as
explanations beyond user–item interactions, and formulated the explanation generation as a …

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

Multi-task learning has been widely applied in computational vision, natural language
processing and other fields, which has achieved well performance. In recent years, a lot of …

The adoption of recommender systems (RSs) in various domains has become increasingly
popular, but concerns have been raised about their lack of transparency and interpretability …

Recommender system helps address information overload problem and satisfy consumers'
personalized requirement in many applications such as e-commerce, social networks and in …

Recently, with the aim of enhancing the trustworthiness of recommender systems,
explainable recommendation has attracted much attention from the research community …

Recommender Systems have become crucial in the modern world, commonly guiding users
towards relevant content or products, and having a large influence over the decisions of …

Current research on explaining sequential recommendations lacks reliable benchmarks and
quantitative metrics, making it difficult to compare explanation performance between …

Explanations are crucial for enhancing user trust and understanding within modern
recommendation systems. To build truly explainable systems, we need high-quality datasets …

Recommender systems with cascading architecture play an increasingly significant role in
online recommendation platforms, where the approach to dealing with negative feedback is …