This is the python implementation -- a POI recommendation model using adversarial training. For the Gowalla dataset, we filter out those users with fewer than 15 check-in POIs and those POIs with fewer than 10 visitors. For Foursquare and Yelp, we discard those users with fewer than 10 check-in POIs and those POIs with fewer than 10 visitors.

proposing an Adversarial POI Recommendation (APOIR) model, con-sisting of two major components: (1) the recommender (R) which suggests POIs based on the learned distribution by maximizing the probabilities that these POIs are predicted as unvisited and poten-tially interested; and (2) the discriminator (D) which distinguishes

2024年10月28日 · The Adversarial POI Recommendation (APOIR) method proposed by Zhou et al. combines reinforcement and adversarial learning methods. The generator recommended POIs that were processed by the discriminator, finally, to update the POIs and the rewards.

2023年4月20日 · APOIR : This strategy is one of the primary POI recommendation systems based on an adversarial learning technique. The APOIR model has two main modules, including a discriminator and a recommender. These modules are able to be trained in a mutual manner for learning user inclination by considering both social relations and geographical.

Adversarial Point-of-Interest Recommendation. Contribute to APOIR2018/APOIR development by creating an account on GitHub.

2022年2月1日 · Zhou et al. [105] proposed Adversarial POI Recommendation (APOIR) which combines GAN, GRU, and Matrix factorization for POI recommendation. GRU and MF combinedly learn both temporal and sequential preferences of users.

在本文中，我们提出了一种新的解耦表示增强注意力网络（DRAN）用于下一个 POI 推荐，它利用解耦表示来显式地建模不同方面和相应的影响，以更精确地表示 POI。 具体来说，我们首先设计了一种传播规则，通过提炼两种类型的 POI 关系图来学习基于图的解耦表示，充分利用基于距离和基于转移的影响进行表示学习。 然后，我们扩展注意力架构以聚合个性化的时空信息，以对下一个时间戳的动态用户偏好进行建模，同时保持解耦表示的不同组件独立。 对两个真实世界数据集 …

2019年5月13日 · In this work, we initiate the first attempt to learn the distribution of user latent preference by proposing an Adversarial POI Recommendation (APOIR) model, consisting of two major components: (1) the recommender (R) which suggests POIs based on the learned distribution by maximizing the probabilities that these POIs are predicted as unvisited ...

2022年3月5日 · 最近，Yu等将一种长短期结构条件生成对抗网络 (conditional generative adversarial network with long - short-term structure, LSTM-CGAN)应用于出租车热点预测，同时捕捉热点的时空变化。 此外，Jin等开发了一种基于上下文的生成模型Crime - GAN，以学习犯罪情境的时空动态。 他们将Seq2Seq、VAE网络和对抗损失结合在框架中，更好地研究时空数据表示。 在此基础上，提出了一种基于深度卷积生成对抗网络 (DCGAN)的海啸时空流体流动预测方法 …

2022年4月15日 · APOIR 51: is the earliest adversarial learning-based POI recommendation pipeline. It entails of two portions, a discriminator and a recommender, which are mutually trained to learn user inclination by playing a minimax game.