Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective
P Sánchez, A Bellogín - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
Point-of-Interest recommendation is an area of increasing research and development
interest within the widely adopted technologies known as Recommender Systems. Among …
interest within the widely adopted technologies known as Recommender Systems. Among …
Personalized recommendation system based on collaborative filtering for IoT scenarios
Z Cui, X Xu, XUE Fei, X Cai, Y Cao… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Recommendation technology is an important part of the Internet of Things (IoT) services,
which can provide better service for users and help users get information anytime …
which can provide better service for users and help users get information anytime …
GeoMF++ scalable location recommendation via joint geographical modeling and matrix factorization
Location recommendation is an important means to help people discover attractive
locations. However, extreme sparsity of user-location matrices leads to a severe challenge …
locations. However, extreme sparsity of user-location matrices leads to a severe challenge …
Attention-based recurrent neural network for location recommendation
Due to the rapid development of Location-Based Social Networks (LBSNs), the Point of
Interest (POI) recommendation has been attracted a lot of research attention. Based on the …
Interest (POI) recommendation has been attracted a lot of research attention. Based on the …
Exploiting pre-trained network embeddings for recommendations in social networks
L Guo, YF Wen, XH Wang - Journal of Computer Science and Technology, 2018 - Springer
Recommender systems as one of the most efficient information filtering techniques have
been widely studied in recent years. However, traditional recommender systems only utilize …
been widely studied in recent years. However, traditional recommender systems only utilize …
Multi-type itemset embedding for learning behavior success
Contextual behavior modeling uses data from multiple contexts to discover patterns for
predictive analysis. However, existing behavior prediction models often face difficulties …
predictive analysis. However, existing behavior prediction models often face difficulties …
[PDF][PDF] Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback.
Matrix factorization has been widely adopted for recommendation by learning latent
embeddings of users and items from observed user-item interaction data. However, previous …
embeddings of users and items from observed user-item interaction data. However, previous …
Leveraging multi-aspect time-related influence in location recommendation
Abstract Point-Of-Interest (POI) recommendation aims to mine a user's visiting history and
find her/his potentially preferred places. Although location recommendation methods have …
find her/his potentially preferred places. Although location recommendation methods have …
A survey of context-aware recommender systems: from an evaluation perspective
X Meng, Y Du, Y Zhang, X Han - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent years, context-aware recommender systems (CARSs), which incorporate
contextual information to achieve better recommendations, become a hot topic in the domain …
contextual information to achieve better recommendations, become a hot topic in the domain …
[PDF][PDF] Point-of-interest recommender systems: A survey from an experimental perspective
P Sánchez, A Bellogín - arXiv preprint arXiv:2106.10069, 2021 - academia.edu
Authors' addresses: Pablo Sánchez, Information Retrieval Group, Universidad Autónoma de
Madrid, Madrid, 28049, Spain, pablo. sanchezp@ uam. es; Alejandro Bellogín, Information …
Madrid, Madrid, 28049, Spain, pablo. sanchezp@ uam. es; Alejandro Bellogín, Information …