research-article

MARAN: : Supporting awareness of users’ routines and preferences for next POI recommendation based on spatial aggregation

Authors:

Dongjin YuAuthors Info & Claims

Volume 238, Issue PF

https://doi.org/10.1016/j.eswa.2023.121961

Published: 27 February 2024 Publication History

Abstract

Next point-of-interest (POI) recommendation has emerged as an essential task in recommender systems with the rapid development of location-based social networks (LBSNs). It has a wide range of applications in smart cities for building personalized scenarios. Current research typically uses sequential relationships to mine user preferences; however, it fails to sufficiently explore the spatial dependence of check-ins and the multi-perspective information they contain. To this end, this study proposes a Multiple Active Region Aware Network (MARAN), a novel routine-aware model for the next POI recommendation that simultaneously captures the user’s routine regularity and short-term preference changes from check-in records. The key to MARAN is its ability to decompose sophisticated user behavior into two parts. One is a stable routine part characterized by central-based graphs built from historical trajectories based on spatial aggregation. The other is an unstable preference part that obtains the user’s recent changes from short-term trajectories. Moreover, a neighborhood-aware negative sampler based on adjacent areas was designed to alleviate spatial sparsity, that is, the imbalance between positive and negative samples during model training. Experiments on two real-world datasets demonstrated that MARAN outperformed state-of-the-art methods.

Highlights

•

Next POI recommendation considering spatial aggregation.

•

User’s routines are characterized by central-based graphs.

•

Users’ recent preferences are obtained from short-term trajectories.

•

Dynamic negative sampling based on current position.

References

[1]

Bahari Sojahrood Z., Taleai M., A POI group recommendation method in location-based social networks based on user influence, Expert Systems with Applications 171 (2021),.

Digital Library

[2]

Bahari Sojahrood Z., Taleai M., Cheng H., Hybrid POI group recommender system based on group type in LBSN, Expert Systems with Applications 219 (2023),.

Digital Library

[3]

Chang B., Jang G., Kim S., Kang J., Learning graph-based geographical latent representation for point-of-interest recommendation, in: Proceedings of the 29th ACM international conference on information & knowledge management, in: CIKM ’20, Association for Computing Machinery, New York, NY, USA, 2020, pp. 135–144,.

Digital Library

[4]

Cheng C., Yang H., Lyu M.R., King I., Where you like to go next: Successive point-of-interest recommendation, in: Proceedings of the twenty-third international joint conference on artificial intelligence, in: IJCAI ’13, AAAI Press, Beijing, China, 2013, pp. 2605–2611.

[5]

Cho E., Myers S.A., Leskovec J., Friendship and mobility: User movement in location-based social networks, in: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, in: KDD ’11, Association for Computing Machinery, New York, NY, USA, 2011, pp. 1082–1090,.

Digital Library

[6]

Cui Q., Zhang C., Zhang Y., Wang J., Cai M., ST-PIL: Spatial-temporal periodic interest learning for next point-of-interest recommendation, in: Proceedings of the 30th ACM international conference on information & knowledge management, ACM, Virtual Event Queensland Australia, 2021, pp. 2960–2964,.

Digital Library

[7]

Dai S., Yu Y., Fan H., Dong J., Personalized POI recommendation: Spatio-temporal representation learning with social tie, in: Jensen C.S., Lim E.-P., Yang D.-N., Lee W.-C., Tseng V.S., Kalogeraki V., Huang J.-W., Shen C.-Y. (Eds.), Database systems for advanced applications, Springer International Publishing, Cham, 2021, pp. 558–574.

[8]

Feng J., Li Y., Zhang C., Sun F., Meng F., Guo A., et al., DeepMove: Predicting human mobility with attentional recurrent networks, in: Proceedings of the 2018 world wide web conference, in: WWW ’18, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 2018, pp. 1459–1468,.

Digital Library

[9]

Gong W., Zheng K., Zhang S., Hu P., Deep pairwise learning for user preferences via dual graph attention model in location-based social networks, Expert Systems with Applications 227 (2023),.

Digital Library

[10]

He J., Li X., Liao L., Song D., Cheung W.K., Inferring a personalized next point-of-interest recommendation model with latent behavior patterns, in: Proceedings of the thirtieth AAAI conference on artificial intelligence, in: AAAI’16, AAAI Press, Phoenix, Arizona, 2016, pp. 137–143.

[11]

He R., McAuley J., Fusing similarity models with Markov chains for sparse sequential recommendation, in: 2016 IEEE 16th international conference on data mining, IEEE Computer Society, Los Alamitos, CA, USA, 2016, pp. 191–200,.

[12]

Huang Z., Ma J., Dong Y., Foutz N.Z., Li J., Empowering next POI recommendation with multi-relational modeling, in: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, in: SIGIR ’22, Association for Computing Machinery, New York, NY, USA, 2022, pp. 2034–2038,.

Digital Library

[13]

Kang W., McAuley J., Self-attentive sequential recommendation, in: 2018 IEEE international conference on data mining, IEEE Computer Society, Los Alamitos, CA, USA, 2018, pp. 197–206,.

[14]

Koren Y., Bell R., Volinsky C., Matrix factorization techniques for recommender systems, Computer 42 (8) (2009) 30–37,.

Digital Library

[15]

Lai H.-C., Lu Y.-S., Wang M.-F., Chen Y.-C., Shih W.-Y., Huang J.-L., SPENT+: A category- and region-aware successive POI recommendation model, in: 2021 22nd asia-pacific network operations and management symposium, 2021, pp. 230–233,.

[16]

Li R., Shen Y., Zhu Y., Next point-of-interest recommendation with temporal and multi-level context attention, in: 2018 IEEE international conference on data mining, 2018, pp. 1110–1115,.

[17]

Li J., Wang Y., McAuley J., Time interval aware self-attention for sequential recommendation, in: Proceedings of the 13th international conference on web search and data mining, in: WSDM ’20, Association for Computing Machinery, New York, NY, USA, 2020, pp. 322–330,.

Digital Library

[18]

Lian D., Wu Y., Ge Y., Xie X., Chen E., Geography-aware sequential location recommendation, in: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, ACM, Virtual Event CA USA, 2020, pp. 2009–2019,.

Digital Library

[19]

Lim N., Hooi B., Ng S.-K., Goh Y.L., Weng R., Tan R., Hierarchical multi-task graph recurrent network for next POI recommendation, in: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, in: SIGIR ’22, Association for Computing Machinery, New York, NY, USA, 2022, pp. 1133–1143,.

Digital Library

[20]

Liu Q., Luo D., Wen T., GholamHosseini H., Qiu X., Li J., POI-3Dgcn: Predicting Odor intensity of monomer flavors based on three-dimensionally embedded graph convolutional network, Expert Systems with Applications 199 (2022),.

Digital Library

[21]

Liu Q., Wu S., Wang L., Tan T., Predicting the next location: A recurrent model with spatial and temporal contexts, in: Proceedings of the thirtieth AAAI conference on artificial intelligence, in: AAAI’16, AAAI Press, Phoenix, Arizona, 2016, pp. 194–200.

[22]

Luo Y., Liu Q., Liu Z., STAN: Spatio-temporal attention network for next location recommendation, in: Proceedings of the web conference 2021, ACM, Ljubljana Slovenia, 2021, pp. 2177–2185,.

Digital Library

[23]

Noulas A., Scellato S., Mascolo C., Pontil M., An empirical study of geographic user activity patterns in foursquare, in: Proceedings of the international AAAI conference on web and social media. Vol. 5, no. 1, 2011, pp. 570–573,.

[24]

Rahmani H.A., Deldjoo Y., di Noia T., The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systems, Expert Systems with Applications 205 (2022),.

Digital Library

[25]

Rao X., Chen L., Liu Y., Shang S., Yao B., Han P., Graph-flashback network for next location recommendation, in: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, ACM, Washington DC USA, 2022, pp. 1463–1471,.

Digital Library

[26]

Rendle S., Factorization machines, in: 2010 IEEE international conference on data mining, 2010, pp. 995–1000,.

Digital Library

[27]

Rendle S., Freudenthaler C., Schmidt-Thieme L., Factorizing personalized Markov chains for next-basket recommendation, in: Proceedings of the 19th international conference on world wide web, in: WWW ’10, Association for Computing Machinery, New York, NY, USA, 2010, pp. 811–820,.

Digital Library

[28]

Scarselli F., Gori M., Tsoi A.C., Hagenbuchner M., Monfardini G., The graph neural network model, IEEE Transactions on Neural Networks 20 (1) (2009) 61–80,.

Digital Library

[29]

Si Y., Zhang F., Liu W., An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features, Knowledge-Based Systems 163 (2019) 267–282,.

[30]

Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., et al., Attention is all you need, in: Guyon I., Luxburg U.V., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R. (Eds.), Advances in neural information processing systems. Vol. 30, Curran Associates, Inc., 2017.

[31]

Wang X., Sun G., Fang X., Yang J., Wang S., Modeling spatio-temporal neighbourhood for personalized point-of-interest recommendation, in: Proceedings of the thirty-first international joint conference on artificial intelligence, International Joint Conferences on Artificial Intelligence Organization, Vienna, Austria, 2022, pp. 3530–3536,.

[32]

Wang Z., Zhu Y., Liu H., Wang C., Learning graph-based disentangled representations for next POI recommendation, in: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, in: SIGIR ’22, Association for Computing Machinery, New York, NY, USA, 2022, pp. 1154–1163,.

Digital Library

[33]

Werneck H., Santos R., Silva N., Pereira A.C., Mourão F., Rocha L., Effective and diverse POI recommendations through complementary diversification models, Expert Systems with Applications 175 (2021),.

Digital Library

[34]

Yang D., Fankhauser B., Rosso P., Cudre-Mauroux P., Location prediction over sparse user mobility traces using RNNs: Flashback in hidden states!, in: Bessiere C. (Ed.), Proceedings of the twenty-ninth international joint conference on artificial intelligence, International Joint Conferences on Artificial Intelligence Organization, 2020, pp. 2184–2190,.

[35]

Yang S., Liu J., Zhao K., Getnext: Trajectory flow map enhanced transformer for next POI recommendation, in: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, ACM, Madrid Spain, 2022, pp. 1144–1153,.

Digital Library

[36]

Yang D., Zhang D., Chen L., Qu B., NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs, Journal of Network and Computer Applications 55 (2015) 170–180,.

[37]

Yu F., Cui L., Guo W., Lu X., Li Q., Lu H., A category-aware deep model for successive POI recommendation on sparse check-in data, in: Proceedings of the web conference 2020, in: WWW ’20, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1264–1274,.

Digital Library

[38]

Zeng J., Zhao Y., Wang Z., Tao H., Gao M., Wen J., LGSA: A next POI prediction method by using local and global interest with spatiotemporal awareness, Expert Systems with Applications 227 (2023),.

Digital Library

[39]

Zhang J.-D., Chow C.-Y., Li Y., LORE: Exploiting sequential influence for location recommendations, in: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems, in: SIGSPATIAL ’14, Association for Computing Machinery, New York, NY, USA, 2014, pp. 103–112,.

Digital Library

[40]

Zhang Y., Dai H., Xu C., Feng J., Wang T., Bian J., et al., Sequential click prediction for sponsored search with recurrent neural networks, in: Proceedings of the twenty-eighth AAAI conference on artificial intelligence, in: AAAI’14, AAAI Press, Québec City, Québec, Canada, 2014, pp. 1369–1375.

[41]

Zhang L., Sun Z., Zhang J., Lei Y., Li C., Wu Z., et al., An interactive multi-task learning framework for next POI recommendation with uncertain check-ins, in: Bessiere C. (Ed.), Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI-20, International Joint Conferences on Artificial Intelligence Organization, 2020, pp. 3551–3557,.

[42]

Zhao P., Zhu H., Liu Y., Xu J., Li Z., Zhuang F., et al., Where to go next: A spatio-temporal gated network for next POI recommendation, in: Proceedings of the AAAI conference on artificial intelligence. Vol. 33, no. 01, 2019, pp. 5877–5884,.

Digital Library

[43]

Zhou Y., Yang G., Yan B., Cai Y., Zhu Z., Point-of-interest recommendation model considering strength of user relationship for location-based social networks, Expert Systems with Applications 199 (2022),.

Digital Library

[44]

Zhu Y., Li H., Liao Y., Wang B., Guan Z., Liu H., et al., What to do next: Modeling user behaviors by time-LSTM, in: Proceedings of the 26th international joint conference on artificial intelligence, in: IJCAI’17, AAAI Press, Melbourne, Australia, 2017, pp. 3602–3608.

Cited By

Wang XFukumoto FCui JSuzuki YYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)NFARec: A Negative Feedback-Aware Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657809(935-945)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657809

Index Terms

MARAN: Supporting awareness of users’ routines and preferences for next POI recommendation based on spatial aggregation
1. Human-centered computing
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

Recommendations

Adaptive Graph Representation Learning for Next POI Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from ...
Learning Graph-based Disentangled Representations for Next POI Recommendation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Next Point-of-Interest (POI) recommendation plays a critical role in many location-based applications as it provides personalized suggestions on attractive destinations for users. Since users' next movement is highly related to the historical visits, ...
SNPR: A Serendipity-Oriented Next POI Recommendation Model
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Next Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend ...

Comments

Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 238, Issue PF

Mar 2024

1588 pages

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 27 February 2024

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang XFukumoto FCui JSuzuki YYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)NFARec: A Negative Feedback-Aware Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657809(935-945)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657809

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents