# Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs

arXiv: https://arxiv.org/abs/2208.13775 · HTML: https://mlsanity.com/paper/2208.13775/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 60 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Insertion-based Decoding with automatically Inferred Generation Order](https://mlsanity.com/paper/1902.01370/) (2019)
- [Smartphone App Usage Prediction Using Points of Interest](https://mlsanity.com/paper/1711.09337/) (2017)
- [Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences](https://mlsanity.com/paper/2202.11485/) (2022)
- [Region Invariant Normalizing Flows for Mobility Transfer](https://mlsanity.com/paper/2109.05738/) (2021)
- [Item Silk Road: Recommending Items from Information Domains to Social Users](https://mlsanity.com/paper/1706.03205/) (2017)
- [ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences](https://mlsanity.com/paper/2206.05291/) (2022)
- [Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer](https://mlsanity.com/paper/2201.06095/) (2022)
- [Position-aware Self-attention with Relative Positional Encodings for Slot Filling](https://mlsanity.com/paper/1807.03052/) (2018)

## Built on by (most cited first, 2 of 2)

- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations](https://mlsanity.com/paper/2501.04762/) (2025)

## Abstract (arXiv)

Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, etc. These models ignore a crucial aspect of spatial mobility -- every user carries their smartphones wherever they go. In addition, with growing privacy concerns, users refrain from sharing their exact geographical coordinates and their social media activity. In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences. This work aligns with the recent psychological studies of online urban users, which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. In addition, our proposal of coarse-grained smartphone data refers to data logs collected in a privacy-conscious manner, i.e., consisting only of (a) category of the smartphone app and (b) category of check-in location. Thus, REVAMP is not privy to precise geo-coordinates, social networks, or the specific application being accessed. Buoyed by the efficacy of self-attention models, we learn the POI preferences of a user using two forms of positional encodings -- absolute and relative -- with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show the predictive prowess of REVAMP and its ability to predict app- and POI categories.