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LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation [article]

Hossein A. Rahmani, Mohammad Aliannejadi, Sajad Ahmadian, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani
2019 arXiv   pre-print
Then, the proposed local geographical model is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation.  ...  Moreover, matrix factorization methods provide effective models which can be used in POI recommendation.  ...  Proposed Method This section presents the proposed POI recommendation method called Local Geographical based Logistic Matrix Factorization (LGLMF). LGLMF consists of two main steps.  ... 
arXiv:1909.06667v1 fatcat:7mbarl3hxzb3nbzbinfiublldi

Considering similarity and the rating conversion of neighbors on neural collaborative filtering

Thitiporn Neammanee, Saranya Maneeroj, Atsuhiro Takasu
2022
The proposed method is compared with the current CF with friends and latent factor model using two types of datasets: real-world and synthetic datasets.  ...  One of the most popular recommender system techniques is collaborative filtering (CF). Nowadays, many researchers apply a neural network with CF, but few focus on the neighbors' concept of CF.  ...  The example of applying the MF technique is the local geographical based logistic MF model (LGLMF) for point of view (POI) recommendation [12] , which is used to detect the probability of user's interest  ... 
doi:10.1371/journal.pone.0266512 pmid:35512009 pmcid:PMC9071140 fatcat:owcxd56nqzg2fc6ocmk4lpotsm