Graph-based recommendation models work well for top-N recommender systems due to
their capability to capture the potential relationships between entities. However, most of the …

Abstract Despite that Machine Learning (ML) applications is not novel, it has gained
popularity partly to the advance in computing processing and cost. Nevertheless, this it is not …

Recommender systems are traditionally optimized to facilitate content discovery for
consumers by ranking items based on predicted relevance. As such, these systems often do …

In recommender systems (RSs), explicit information is often preferred over implicit because it
is much more accurate than implicit or predicted information; for example, the user can enter …

Currently, collaborative filtering technology has been widely used in personalized
recommender systems. The problem of data sparsity is a severe challenge faced by …

目的潜在偏好, 优点是不需要对项目进行复杂的特征提取[6-7]. 协同过滤仅需要利用用户的历史
评分数据, 因此简单有效. 但是, 也存在数据的稀疏问题和冷启动问题[7]. 且推荐算法对捕捉用户 …

In real-world recommender systems, user preferences are dynamic and typically change
over time. Capturing the temporal dynamics of user preferences is essential to design an …

With the explosion of information, recommendation systems have become important for
users to find their interested information. Existing recommendation methods mainly utilize …

Recent years have witnessed the great success of group buying (GB) in social e-commerce,
opening up a new way of online shopping. In this business model, a user can launch a GB …

Abstract Knowledge graph (KG)-based recommendation methods effectively alleviate the
data sparsity and cold-start problems in collaborative filtering. Among these methods …