Knowledge Graphs (KGs) have been utilized as useful side information to improve
recommendation quality. In those recommender systems, knowledge graph information …

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

Conversational recommender systems (CRS) aim to recommend high-quality items to users
through interactive conversations. Although several efforts have been made for CRS, two …

In this paper, we introduce a new self-supervised rationalization method, called KGRec, for
knowledge-aware recommender systems. To effectively identify informative knowledge …

Properly handling missing data is a fundamental challenge in recommendation. Most
present works perform negative sampling from unobserved data to supply the training of …

For sequential recommendation, it is essential to capture and predict future or long-term user
preference for generating accurate recommendation over time. To improve the predictive …

The recommender system has recently drawn a lot of attention to the communities of
information services and mobile applications. Many deep learning-based recommendation …

Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but more …

Compared with the traditional recommendation system, sequential recommendation holds
the ability of capturing the evolution of users' dynamic interests. Many previous studies in …

Recently, deep learning has made significant progress in the task of sequential
recommendation. Existing neural sequential recommenders typically adopt a generative …