research-article

From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data

Authors:

Depeng JinAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 1

Article No.: 26, Pages 1 - 22

https://doi.org/10.1145/3314413

Published: 29 March 2019 Publication History

Abstract

With increasing diversity of user interest and preference, personalized location recommendation is essential and beneficial to our daily life. To achieve this, the most critical challenge is the cold-start recommendation problem, for we cannot learn preference from cold-start users without any historical records. In this paper, we demonstrate that it is feasible to make personalized location recommendation by learning user interest and location features from app usage data. By proposing a novel generative model to transfer user interests from app usage behavior to location preference, we achieve personalized location recommendation via learning the interest's correlation between locations and apps. Based on two real-world datasets, we evaluate our method's performance with a variety of scenarios and parameters. The results demonstrate that our method outperforms the state-of-the-art solutions in solving cold-start problem, i.e., when there are 60% cold-start users, we can still achieve a 77.0% hitrate in recommending the top five locations, which is at least 9.6% higher than the baselines. Our study is the first step forward for transferring user interests learning from online fingerprints to offline footprints, which paves the way for better personalized location recommendation services.

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Cited By

Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679893
Ouyang YGuo BWang QLiang YYu Z(2023)Learning Dynamic App Usage Graph for Next Mobile App RecommendationIEEE Transactions on Mobile Computing10.1109/TMC.2022.316111422:8(4742-4753)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3161114
Gupta VBedathur S(2023)Modeling Spatial Trajectories Using Coarse-Grained Smartphone LogsIEEE Transactions on Big Data10.1109/TBDATA.2022.32047599:2(608-620)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3204759
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Index Terms

From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering
    2. Decision support systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 1

March 2019

786 pages

EISSN:2474-9567

DOI:10.1145/3323054

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Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 29 March 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 August 2018

Published in IMWUT Volume 3, Issue 1

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research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology
National Key Research and Development Program of China
National Nature Science Foundation of China
Beijing National Research Center for Information Science and Technology

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Cited By

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https://dl.acm.org/doi/10.1145/3627673.3679893
Ouyang YGuo BWang QLiang YYu Z(2023)Learning Dynamic App Usage Graph for Next Mobile App RecommendationIEEE Transactions on Mobile Computing10.1109/TMC.2022.316111422:8(4742-4753)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3161114
Gupta VBedathur S(2023)Modeling Spatial Trajectories Using Coarse-Grained Smartphone LogsIEEE Transactions on Big Data10.1109/TBDATA.2022.32047599:2(608-620)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3204759
Chen JLin KChen XWang XHsu T(2023)Location Recommendations Based on Multi-view Learning and Attention-Enhanced Graph NetworksBig Data and Social Computing10.1007/978-981-99-3925-1_5(83-95)Online publication date: 30-Jun-2023
https://doi.org/10.1007/978-981-99-3925-1_5
Chen XChen JLian XMai W(2022)Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location RecommendationsApplied Sciences10.3390/app1214688212:14(6882)Online publication date: 7-Jul-2022
https://doi.org/10.3390/app12146882
Pan LYu CLi JHuang TBi XShi Y(2022)Automatically Generating and Improving Voice Command Interface from Operation Sequences on SmartphonesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517459(1-21)Online publication date: 29-Apr-2022
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Lu XLiu JGan SLi TXiao YLiu Y(2022)Recommendation Model Based on Dynamic Interest Group Identification and Data CompensationIEEE Transactions on Network and Service Management10.1109/TNSM.2021.311270219:1(89-99)Online publication date: Mar-2022
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Shaygan MMeese CLi WZhao XNejad M(2022)Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunitiesTransportation Research Part C: Emerging Technologies10.1016/j.trc.2022.103921145(103921)Online publication date: Dec-2022
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Abdullah NRasheed RNasir MRahman M(2021)Eliciting Auxiliary Information for Cold Start User Recommendation: A SurveyApplied Sciences10.3390/app1120960811:20(9608)Online publication date: 15-Oct-2021
https://doi.org/10.3390/app11209608
Choi SLee DPark C(2020)On the Smaller Number of Inputs for Determining User Preferences in Recommender SystemsMathematics10.3390/math81221388:12(2138)Online publication date: 1-Dec-2020
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