# Spatial Object Recommendation with Hints: When Spatial Granularity Matters

arXiv: https://arxiv.org/abs/2101.02969 · HTML: https://mlsanity.com/paper/2101.02969/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Neural Ranking Models with Weak Supervision](https://mlsanity.com/paper/1704.08803/) (2017)
- [Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction](https://mlsanity.com/paper/1911.10516/) (2019)
- [Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence](https://mlsanity.com/paper/1809.10770/) (2018)
- [Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction](https://mlsanity.com/paper/1811.06912/) (2018)
- [Early Warning of Human Crowds Based on Query Data from Baidu Map: Analysis Based on Shanghai Stampede](https://mlsanity.com/paper/1603.06780/) (2016)
- [GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation](https://mlsanity.com/paper/1606.05859/) (2016)
- [PERS: A Personalized and Explainable POI Recommender System](https://mlsanity.com/paper/1712.07727/) (2017)

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

- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [Out-of-Town Recommendation with Travel Intention Modeling](https://mlsanity.com/paper/2101.12555/) (2021)
- [HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning](https://mlsanity.com/paper/2406.10616/) (2024)
- [Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks](https://mlsanity.com/paper/2202.13686/) (2022)
- [Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation](https://mlsanity.com/paper/2409.10343/) (2024)
- [Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction](https://mlsanity.com/paper/2409.15764/) (2024)
- [Multi-Temporal Relationship Inference in Urban Areas](https://mlsanity.com/paper/2306.08921/) (2023)
- [OpenSiteRec: An Open Dataset for Site Recommendation](https://mlsanity.com/paper/2307.00856/) (2023)
- [Adversarial Neural Trip Recommendation](https://mlsanity.com/paper/2109.11731/) (2021)
- [Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models](https://mlsanity.com/paper/2511.07295/) (2025)
- [AskNearby: An LLM-Based Application for Neighborhood Information Retrieval and Personalized Cognitive-Map Recommendations](https://mlsanity.com/paper/2512.02502/) (2025)

## Abstract (arXiv)

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods.