# Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

arXiv: https://arxiv.org/abs/1809.10770 · HTML: https://mlsanity.com/paper/1809.10770/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [Knowledge-Enhanced Top-K Recommendation in Poincar'e Ball](https://mlsanity.com/paper/2101.04852/) (2021)
- [Spatial Object Recommendation with Hints: When Spatial Granularity Matters](https://mlsanity.com/paper/2101.02969/) (2021)
- [Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation](https://mlsanity.com/paper/2101.04849/) (2021)
- [Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity](https://mlsanity.com/paper/2407.19668/) (2024)
- [Robust Graph Clustering via Meta Weighting for Noisy Graphs](https://mlsanity.com/paper/2311.00322/) (2023)
- [Traffic Accident Risk Forecasting using Contextual Vision Transformers](https://mlsanity.com/paper/2209.11180/) (2022)
- [Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data](https://mlsanity.com/paper/2404.04271/) (2024)
- [A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation](https://mlsanity.com/paper/2201.08150/) (2022)
- [TransTARec: Time-Adaptive Translating Embedding Model for Next POI Recommendation](https://mlsanity.com/paper/2404.07096/) (2024)
- [Adversarial Neural Trip Recommendation](https://mlsanity.com/paper/2109.11731/) (2021)
- [Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating](https://mlsanity.com/paper/2110.06523/) (2021)

## Abstract (arXiv)

The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services. However, with the tremendous increase of users and POIs, POI recommender systems still face several challenging problems: (1) the hardness of modeling non-linear user-POI interactions from implicit feedback; (2) the difficulty of incorporating context information such as POIs' geographical coordinates. To cope with these challenges, we propose a novel autoencoder-based model to learn the non-linear user-POI relations, namely textitSAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD). In particular, unlike previous works equally treat users' checked-in POIs, our self-attentive encoder adaptively differentiates the user preference degrees in multiple aspects, by adopting a multi-dimensional attention mechanism. To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel. To evaluate the proposed model, we conduct extensive experiments on three real-world datasets with many state-of-the-art baseline methods and evaluation metrics. The experimental results demonstrate the effectiveness of our model.