Where You Like to Go Next: Successive Point-of-Interest Recommendation
By Chen-Kuang Cheng, Haiqin Yang, Michael R. Lyu, Irwin King. International Joint Conference on Artificial Intelligence, 2013
478 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/01a0438352146dea1129121a94fe428d97971547
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Xiangnan He
- Malte Ludewig
- Fajie Yuan
- Dietmar Jannach
- Massimo Quadrana
- Alexandros Karatzoglou
- Shoujin Wang
- Paolo Cremonesi
Citations in the corpus
- Sequence-Aware Recommender Systems (2018, methodology): “, based on the user’s current location [21, 43]).”
- DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018, unknown): “[5] propose a matrix factorization method”
- Sequential Recommendation with User Memory Networks (2018, unknown): “Beyond e-commerce, sequential recommendation has also been applied to various application scenarios such as POI recommendation [3, 4], music recommendation [1, 8, 29], browsing recommendation [35], etc.”
- A Simple Convolutional Generative Network for Next Item Recommendation (2018, methodology): “Early work in sequential recommendations mostly rely on the markov chain [5] and feature-based matrix factorization [12, 32–34] approaches.”
- Evaluation of session-based recommendation algorithms (2018, methodology): “To overcome the limitations of pure sequence learning methods, a number of hybrid methods were proposed that, for instance, combine the advantages of matrix factorization techniques with sequence modeling approaches in the form of Factorized Markov Chains [6, 17, 19, 33, 45].”
- A Survey on Session-based Recommender Systems (2019, unknown): “[16] extended FPMC into FPMC-LR by adding a constraint to limit user movements into a localized region to make it more consistent with the real-world tourism cases for next POI recommendations.”
- Hierarchical Gating Networks for Sequential Recommendation (2019, background): “The Markov chain [2] is a classical option to solve this problem.”
- STAN: Spatio-Temporal Attention Network for Next Location Recommendation (2021, background): “Matrix factorization models [15, 26] are proposed to approach this problem, with further extensions [3, 10] find that explicit spatial and temporal information help a lot with recommendation performance.”
- Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016, methodology): “And PFMC improves the performance greatly comparing with TF. PFMC-LR and PRME achieve further improvement with via incorporating distance information.”