Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available …
Q Zhang, J Lu, Y Jin - Complex & Intelligent Systems, 2021 - Springer
Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial …
G Hu, Y Zhang, Q Yang - Proceedings of the 27th ACM international …, 2018 - dl.acm.org
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 …
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
H Fang, D Zhang, Y Shu, G Guo - ACM Transactions on Information …, 2020 - dl.acm.org
In the field of sequential recommendation, deep learning--(DL) based methods have received a lot of attention in the past few years and surpassed traditional models such as …
Recommender systems use past behaviors of users to suggest items. Most tend to offer items similar to the items that a target user has indicated as interesting. As a result, users …
The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each …
W Fu, Z Peng, S Wang, Y Xu, J Li - … of the AAAI Conference on Artificial …, 2019 - ojs.aaai.org
As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research …
Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map …