# Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

## TL;DR

This paper presents a method to predict city-wide shopping patterns by integrating mobility data and spatial interactions, addressing data sparsity and revealing urban lifestyle insights.

## Contribution

It introduces a novel Collective Matrix Factorization approach with interaction regularization to fuse mobility and shopping data for urban pattern prediction.

## Key findings

- Model outperforms baseline methods on standard metrics.
- Reveals divergent shopping demands across urban regions.
- Enhances understanding of urban lifestyles through data integration.

## Abstract

What people buy is an important aspect or view of lifestyles. Studying people's shopping patterns in different urban regions can not only provide valuable information for various commercial opportunities, but also enable a better understanding about urban infrastructure and urban lifestyle. In this paper, we aim to predict city-wide shopping patterns. This is a challenging task due to the sparsity of the available data -- over 60% of the city regions are unknown for their shopping records. To address this problem, we incorporate another important view of human lifestyles, namely mobility patterns. With information on "where people go", we infer "what people buy". Moreover, to model the relations between regions, we exploit spatial interactions in our method. To that end, Collective Matrix Factorization (CMF) with an interaction regularization model is applied to fuse the data from multiple views or sources. Our experimental results have shown that our model outperforms the baseline methods on two standard metrics. Our prediction results on multiple shopping patterns reveal the divergent demands in different urban regions, and thus reflect key functional characteristics of a city. Furthermore, we are able to extract the connection between the two views of lifestyles, and achieve a better or novel understanding of urban lifestyles.