Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

摘要深度学习是机器学习领域一个重要研究方向, 近年来在图像处理, 自然语言理解,
语音识别和在线广告等领域取得了突破性进展. 将深度学习融入推荐系统中 …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Recommender system is a specific type of intelligent systems, which exploits historical user
ratings on items and/or auxiliary information to make recommendations on items to the …

Cross domain recommender systems have been increasingly valuable for helping
consumers identify the most satisfying items from different categories. However, previously …

Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …

The cross-domain recommendation technique is an effective way of alleviating the data
sparse issue in recommender systems by leveraging the knowledge from relevant domains …

Collaborative filtering (CF) is a successful approach commonly used by many recommender
systems. Conventional CF-based methods use the ratings given to items by users as the …

Data sparsity is a challenge problem that most modern recommender systems are
confronted with. By leveraging the knowledge from relevant domains, the cross-domain …