GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation
By Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, Yong Rui. Knowledge Discovery and Data Mining, 2014
657 citations recorded by the source corpus.
Subfields: Information Systems, Transportation
View Paper: https://www.semanticscholar.org/paper/f9ffa39eb3b4fb2764e4ce1d8e98258f7ae91d4d
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Citations in the corpus
- Learning Graph-based POI Embedding for Location-based Recommendation (2016, background): “Specifically, to overcome the data sparsity issue, most prior work of location-based recommendation focused on exploiting the geographical influence [10, 3, 20] and temporal cyclic effect [7, 28] to provide spatial or/and temporal context-aware recommendation.”
- Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation (2020, background): “Early studies in POI recommendation focus mainly on estimating users’ preferences using Collaborative Filtering (CF), especially Matrix Factorization (MF) based techniques (Ye et al. 2011; Cheng et al. 2012; Lian et al. 2014; Gao et al. 2015).”
- Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (2015, methodology): “[11] propose a model, namely GeoMF, based on Weighted Matrix Factorization (WMF), and the geographical influence is incorporated into WMF.”
- Representation learning via Dual-Autoencoder for recommendation (2017, background): “…there is a surge of hybrid recommendation, such as social recommendation (Ma, Yang, Lyu, & King, 2008; Ma, Zhou, Lyu, & King, 2011), location based recommendation (Lian et al., 2014; Liu, Fu, Yao, &Xiong, 2013), andheterogeneous network based recommendation (Shi et al., 2015; Yu et al., 2014).”
- Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, background): “While traditional recommender systems mostly work with ratings and reviews [20], POI recommendations naturally come with various context, such as geographical information [24, 25, 28, 47, 49], categorical information [53, 55], social information [22, 38, 50, 52], temporal information [48, 56], etc.”
- Personalized Ranking Metric Embedding for Next New POI Recommendation (2015, methodology): “Another CF method is Matrix Factorization [Cheng et al., 2012; Lian et al., 2014], which learns the general taste of a user by factorizing the observed user-item preference matrix.”
- GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, positive): “Another way is to apply the geographical latent factor or topic models to derive latent features of regions or POIs [7, 8, 12, 16, 25, 26].”
- Practical Privacy Preserving POI Recommendation (2020, methodology): “im to learn user and item latent factors based on known user-item action histories such as ratings and clicks. Popular factorization based CF models include Matrix Factorization (MF) and its variants [13, 30, 34, 39, 61], regression-based latent factor models [1], Bayesian personalized ranking [46],”
- Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (2019, background): “Different from traditional recommendations (e.g., movie recommendation, music recommendation), POI recommendation is characterized by geographic information and no explicit rating information (Ye et al. 2011; Lian et al. 2014).”
- Collaborative Knowledge Base Embedding for Recommender Systems (2016, methodology): “For each dataset, similar to [14], we randomly select 70% items associated with each user to constitute the training set and use all the remaining as the test set.”