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Mining Shopping Patterns for Divergent Urban Regions by - - PowerPoint PPT Presentation

Mar 28, 2024 117 likes •420 views

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data Author : Haishan Liu,Tianran Hu, Ruihua Song, Yingzi Wang, Xing Xie, Jiebo Luo Source : CIKM 16 Advisor : Jia-Ling Koh Speaker : Chia-Yi Huang

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Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

Author : Haishan Liu,Tianran Hu, Ruihua Song, Yingzi Wang, Xing Xie, Jiebo Luo Source : CIKM’ 16 Advisor : Jia-Ling Koh Speaker : Chia-Yi Huang Date : 2017/08/29

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Outline

▸ Introduction

▸ Method ▸ Experiment ▸ Conclusion

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Introduction

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▸ Motivation

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Introduction

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▸ Shopping Pattern : <Tables, Photography,

Digital accessories,…>

▸ Mobility Pattern : <School Dormitories,

School Libraries,…>

▸ Region

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Introduction

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▸ Market Basket Analysis

▸ Consumers usually have demands for a group

  • f products

▸ People’s demands are highly related to their

lives

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Outline

▸ Introduction

▸ Method

▸ Experiment ▸ Conclusion

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Method

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Method

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▸ Shopping Patterns Extraction

▸ Browsing log of 


shopping website

▸ NMF ▸ Ps ▸ Coefficient Matrix ▸ Rs : sum up the weight of location 


in the same region.

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Method

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▸ Mobility Patterns Extraction

▸ User ID, POI category,


latitude, longitude

▸ NMF ▸ Pm ▸ Coefficient Matrix ▸ Rm : sum up the weight 


  • f user in the same region.

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Method

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▸ Collective Matrix Factorization

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Method

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▸ Collective Matrix Factorization

▸ d

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Method

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▸ City-wide Interaction Regularization

▸ Gravity Model ▸ City-wide Interaction Regularization

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Method

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▸ Gravity Model

▸ Oi , the number of individuals leaving region i ▸ Dj , the number of individuals arriving at region j ▸ The distance between two regions,

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Method

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▸ City-wide Interaction Regularization

▸ The more interactions between two regions, the

more alike their lifestyles are.

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Method

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▸ Hybrid Model

▸ Combine the collective matrix factorization

and interaction regularization

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Method

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Outline

▸ Introduction ▸ Method

▸ Experiment

▸ Conclusion

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▸ Data Set

▸ Online browsing dataset : 250 product

categories

▸ Check-in dataset : 1.5 million check-in data, 200

POI categories

▸ Bus dataset : 3 million bus-trip records ▸ Taxi dataset : 1.9 million taxi-trip records

Experiment

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▸ Baseline

▸ Matrix Factorization (MF) ▸ Collective Matrix Factorization (CMF) ▸ CMF with neighboring information

Experiment

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▸ Evaluation

▸ 





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Experiment

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Experiment

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Experiment

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Experiment

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Experiment

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Experiment

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Outline

▸ Introduction ▸ Method ▸ Experiment

▸ Conclusion

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Conclusion

▸ Connecting the shopping patterns with the

mobility patterns in a region.

▸ Modeling the interactions between regions, and

leverage the information of known regions to infer the shopping patterns in unknown regions.

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THANK YOU