research-article

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

Authors:

Jiawei HanAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1245 - 1254

https://doi.org/10.1145/3097983.3098094

Published: 04 August 2017 Publication History

Abstract

Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.

References

[1]

Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani 2006. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of machine learning research Vol. 7, Nov (2006), 2399--2434.

Digital Library

[2]

Robert M Bell and Yehuda Koren 2007. Lessons from the Netflix prize challenge. Acm Sigkdd Explorations Newsletter Vol. 9, 2 (2007), 75--79.

Digital Library

[3]

Dimitri P Bertsekas. 2014. Constrained optimization and Lagrange multiplier methods. Academic press.

[4]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 translating embeddings for modeling multi-relational data Advances in neural information processing systems. 2787--2795.

[5]

Ting Chen and Yizhou Sun 2017. Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification WSDM. ACM.

[6]

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, and others 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 7--10.

Digital Library

[7]

Eunjoon Cho, Seth A Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1082--1090.

[8]

Ronan Collobert and Jason Weston 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning. ACM, 160--167.

Digital Library

[9]

Ali Mamdouh Elkahky, Yang Song, and Xiaodong He 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems Proceedings of the 24th International Conference on World Wide Web. ACM, 278--288.

[10]

Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan 2010. Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence, Vol. 32, 9 (2010), 1627--1645.

Digital Library

[11]

Aditya Grover and Jure Leskovec 2016. node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855--864.

Digital Library

[12]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.

[13]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat Seng Chua. 2017. Neural Collaborative Filtering. In International Conference on World Wide Web. 173--182.

Digital Library

[14]

Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua 2016. Fast matrix factorization for online recommendation with implicit feedback Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 549--558.

[15]

Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, and others 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine Vol. 29, 6 (2012), 82--97.

[16]

Liangjie Hong, Aziz S Doumith, and Brian D Davison. 2013. Co-factorization machines: modeling user interests and predicting individual decisions in twitter Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 557--566.

[17]

Yifan Hu, Yehuda Koren, and Chris Volinsky 2008. Collaborative filtering for implicit feedback datasets Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 263--272.

[18]

Ming Ji, Yizhou Sun, Marina Danilevsky, Jiawei Han, and Jing Gao 2010. Graph regularized transductive classification on heterogeneous information networks Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 570--586.

[19]

D Kinga and J Ba Adam. 2015. A method for stochastic optimization. In International Conference on Learning Representations (ICLR).

[20]

Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 426--434.

[21]

Omer Levy and Yoav Goldberg 2014. Neural word embedding as implicit matrix factorization Advances in neural information processing systems. 2177--2185.

[22]

Huayu Li, Yong Ge, and Hengshu Zhu 2016. Point-of-Interest Recommendations: Learning Potential Check-ins from Friends Proceedings of the 22th ACM SIGKDD international conference on on Knowledge discovery and data mining. ACM.

[23]

Sheng Li, Jaya Kawale, and Yun Fu 2015. Deep collaborative filtering via marginalized denoising auto-encoder Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 811--820.

[24]

Xutao Li, Gao Cong, Xiao-Li Li, Tuan-Anh Nguyen Pham, and Shonali Krishnaswamy 2015. Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 433--442.

Digital Library

[25]

Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 831--840.

Digital Library

[26]

Dawen Liang, Laurent Charlin, James McInerney, and David M Blei 2016. Modeling user exposure in recommendation. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 951--961.

Digital Library

[27]

Binbin Lin, Ji Yang, Xiaofei He, and Jieping Ye. 2014. Geodesic Distance Function Learning via Heat Flow on Vector Fields ICML.

[28]

Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014. Exploiting geographical neighborhood characteristics for location recommendation Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 739--748.

[29]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119.

[30]

Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang 2008. One-class collaborative filtering. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, 502--511.

Digital Library

[31]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations SIGKDD. ACM, 701--710.

Digital Library

[32]

Dinh Q Phung, Svetha Venkatesh, and others 2009. Ordinal Boltzmann machines for collaborative filtering Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 548--556.

[33]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme 2009. BPR: Bayesian personalized ranking from implicit feedback Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 452--461.

[34]

Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted Boltzmann machines for collaborative filtering Proceedings of the 24th international conference on Machine learning. ACM, 791--798.

[35]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl 2001. Item-based collaborative filtering recommendation algorithms Proceedings of the 10th international conference on World Wide Web. ACM, 285--295.

[36]

Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie 2015. Autorec: Autoencoders meet collaborative filtering Proceedings of the 24th International Conference on World Wide Web. ACM, 111--112.

Digital Library

[37]

Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng 2013. Reasoning with neural tensor networks for knowledge base completion Advances in neural information processing systems. 926--934.

[38]

Jiliang Tang, Xia Hu, Huiji Gao, and Huan Liu. 2013. Exploiting Local and Global Social Context for Recommendation IJCAI. 264--269.

[39]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. WWW. ACM, 1067--1077.

Digital Library

[40]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung 2015. Collaborative deep learning for recommender systems Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235--1244.

[41]

Xinxi Wang and Ye Wang 2014. Improving content-based and hybrid music recommendation using deep learning Proceedings of the 22nd ACM international conference on Multimedia. ACM, 627--636.

[42]

Jason Weston, Frédéric Ratle, Hossein Mobahi, and Ronan Collobert 2012 Deep learning via semi-supervised embedding. Neural Networks: Tricks of the Trade. Springer, 639--655.

[43]

Derry Wijaya, Partha Pratim Talukdar, and Tom Mitchell 2013. Pidgin: ontology alignment using web text as interlingua Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM, 589--598.

[44]

Yao Wu, Christopher Dubois, Alice X Zheng, and Martin Ester 2016. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems ACM International Conference on Web Search and Data Mining. 153--162.

[45]

Dingqi Yang, Daqing Zhang, Longbiao Chen, and Bingqing Qu 2015. NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs. Journal of Network and Computer Applications Vol. 55 (2015), 170--180.

[46]

Zhilin Yang, William Cohen, and Ruslan Salakhutdinov. 2016. Revisiting semi-supervised learning with graph embeddings ICML.

[47]

Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 325--334.

[48]

Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann 2013. Time-aware point-of-interest recommendation. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, 363--372.

Digital Library

[49]

Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, and Jiawei Han. 2017. Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 361--370.

Digital Library

[50]

C. Zhang, K. Zhang, Q. Yuan, L. Zhang, T Hanratty, and J. Han 2016. GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media Proceedings of the 22th ACM SIGKDD international conference on on Knowledge discovery and data mining. ACM. 1305.

Digital Library

[51]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 353--362.

[52]

Jia-Dong Zhang and Chi-Yin Chow 2013. iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 334--343.

Digital Library

[53]

Jia-Dong Zhang and Chi-Yin Chow 2015. GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 443--452.

Digital Library

[54]

Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li 2014. Lore: Exploiting sequential influence for location recommendations Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 103--112.

[55]

Kaiqi Zhao, Gao Cong, Quan Yuan, and Kenny Q Zhu. 2015. SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews Data Engineering (ICDE), 2015 IEEE 31st International Conference on. IEEE, 675--686.

[56]

Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R Lyu, and Irwin King 2016. Stellar: spatial-temporal latent ranking for successive point-of-interest recommendation Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, 315--321.

[57]

Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016. A neural autoregressive approach to collaborative filtering Proceedings of the 33nd International Conference on Machine Learning. 764--773.

[58]

Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf. 2003. Learning with local and global consistency. In NIPS, Vol. Vol. 16. 321--328.

Digital Library

[59]

Xiaojin Zhu, Zoubin Ghahramani, John Lafferty, and others 2003. Semi-supervised learning using gaussian fields and harmonic functions ICML, Vol. Vol. 3. 912--919.

Digital Library

Cited By

Han XZhang YMa CShang XCheng RGrubenmann TLi X(2025)Hypergraph-Enhanced Multi-Granularity Stochastic Weight Completion in Sparse Road NetworksACM Transactions on Knowledge Discovery from Data10.1145/3719013Online publication date: 21-Feb-2025
https://doi.org/10.1145/3719013
Aljunid MD.H. MHooshmand MAli WShetty AAlzoubah S(2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128718
Dietz LSánchez PBellogín A(2025)Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithmsInformation Technology & Tourism10.1007/s40558-024-00304-0Online publication date: 3-Jan-2025
https://doi.org/10.1007/s40558-024-00304-0
Show More Cited By

Index Terms

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
    2. Natural language processing
  2. Machine learning
2. Information systems

Recommendations

Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Semi-supervised collaborative filtering ensemble
Abstract
Collaborative filtering (CF) plays a central role in recommender systems, but often suffers from the data sparsity issue that dramatically degrades the recommendation performance. In this paper, we propose a Semi-Supervised Ensemble Filtering (...
Merging trust in collaborative filtering to alleviate data sparsity and cold start

Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NIGMS
U.S. Army Research Lab

Conference

KDD '17

Sponsor:

KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

Acceptance Rates

KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

224
Total Citations
View Citations
2,711
Total Downloads

Downloads (Last 12 months)92
Downloads (Last 6 weeks)5

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Han XZhang YMa CShang XCheng RGrubenmann TLi X(2025)Hypergraph-Enhanced Multi-Granularity Stochastic Weight Completion in Sparse Road NetworksACM Transactions on Knowledge Discovery from Data10.1145/3719013Online publication date: 21-Feb-2025
https://doi.org/10.1145/3719013
Aljunid MD.H. MHooshmand MAli WShetty AAlzoubah S(2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128718
Dietz LSánchez PBellogín A(2025)Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithmsInformation Technology & Tourism10.1007/s40558-024-00304-0Online publication date: 3-Jan-2025
https://doi.org/10.1007/s40558-024-00304-0
Ammar KInoubli WZghal SNguifo E(2025)Navigating complexity: a comprehensive review of heterogeneous information networks and embedding techniquesKnowledge and Information Systems10.1007/s10115-025-02357-xOnline publication date: 13-Feb-2025
https://doi.org/10.1007/s10115-025-02357-x
Qin WLuo X(2024)Asynchronous Parallel Fuzzy Stochastic Gradient Descent for High-Dimensional Incomplete Data RepresentationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.330037032:2(445-459)Online publication date: Feb-2024
https://doi.org/10.1109/TFUZZ.2023.3300370
Sun Y(2024)TransTARec: Time-Adaptive Translating Embedding Model for Next POI Recommendation2024 5th International Conference on Computer Engineering and Application (ICCEA)10.1109/ICCEA62105.2024.10603711(647-651)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCEA62105.2024.10603711
Ammar KInoubli WZghal SBorji ANguifo E(2024)Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs.Procedia Computer Science10.1016/j.procs.2023.10.098225:C(1104-1113)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.098
Kumar AJain DMallik AKumar S(2024)Modified node2vec and attention based fusion framework for next POI recommendationInformation Fusion10.1016/j.inffus.2023.101998101(101998)Online publication date: Jan-2024
https://doi.org/10.1016/j.inffus.2023.101998
Halder SLim KChan JZhang X(2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
https://doi.org/10.1016/j.asoc.2023.111200
Acharya MMohbey K(2024)Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive reviewGeoInformatica10.1007/s10707-024-00531-xOnline publication date: 28-Oct-2024
https://doi.org/10.1007/s10707-024-00531-x
Show More Cited By

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten