With the increasing abundance of information resources and the development of deep
learning techniques, recommender systems (RSs) based on deep learning have gradually …

The recommendation system makes recommendations based on the preferences of the
users. These user preferences usually come from the user's basic information, item rating …

The evaluation of semantic relatedness between web resources is still an open challenge.
This paper focuses on knowledge-based methods that provide an alternative to corpus …

Abstract The adoption of Recommender Systems (RSs) in various services is imperative in
the present era. One of the inherent challenge faced by RSs is the user cold start problem …

基于迁移学习的跨域推荐可以有效地学习连接源域和目标域的映射函数, 但其性能仍然受到表征
质量不高和负迁移问题的影响, 不能有效地为冷启动用户进行推荐, 为此提出了一种融合信息 …

Deep learning allows the recommender system to discover deep patterns and
representations from data, capturing complex interactions between users and objects across …

Deep learning allows the recommender system to discover deep patterns and
representations from data, capturing complex interactions between users and objects across …

A deep semantic-level cross-domain recommendation model based on DSV-CDRM is
proposed to address the problems of existing methods such as single modeling approach …

Under big scholarly data, a longstanding challenge is to help researchers to find scientific
articles they would like. Current information retrieval and recommendation systems have …