Locus recommendation using probabilistic matrix factorization techniques

Rachna Behl; Indu Kashyap

doi:10.16925/2357-6014.2021.01.10

Locus recommendation using probabilistic matrix factorization techniques

Research Articles

https://doi.org/10.16925/2357-6014.2021.01.10

Rachna Behl Ver Biografía

Manav Rachna International Institute of Research and Studies

Indu Kashyap Ver Biografía

Department of Computer Science and Engineering

Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic

Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20.

Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to

the users.

Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF.

This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well.

Conclusion: The motive of the work is to identify the best technique for recommending locations with the

highest accuracy and allow users to choose from a plethora of available locations; the best and interesting

location based on the individual’s profile.

Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models.

Limitations: User’s contextual information like demographics, social and geographical preferences have

not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.

Keywords: collaborative filtering, information filtering, LBSN, matrix factorization, POI

R. Wilken, “Places nearby: Facebook as a location-based social media platform,” New Media & Society, vol,16, no. 7, 1087-1103, 2014.

W. R. Tobler, “A computer movie simulating urban growth in the Detroit region,” Economic geography, vol. 46, sup.1, 1970, 234-240.

M. J. Pazzani, D. Billsus, “Content-Based Recommendation Systems,” in Brusilovsky P., Kobsa A., Nejdl W. (eds)The Adaptive Web. Lecture Notes in Computer Science, vol. 4321, 2007, Springer, Berlin, Heidelberg, doi: https://doi.org/10.1007/978-3-540-72079-9_10.

G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, June 2005, doi: 10.1109/TKDE.2005.99.

Z. Huang, D. Zeng and H. Chen, “A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce,” IEEE Intelligent Systems, vol. 22, no. 5, pp. 68-78, Sept.-Oct. 2007, doi: 10.1109/MIS.2007.4338497.

X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Adv. Artif. Intell. 2009, Article 4, January 2009, doi: https://doi.org/10.1155/2009/421425.

S. Vucetic and Z. Obradovic, “Collaborative filtering using a regression-based approach,” Knowledge and Information Systems, vol. 7, no. 1, 1-22, 2005.

H. Gao, J. Tang, X. Hu, and H. Liu, “Content-aware point of interest recommendation on location-based social networks,” Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 1721–1727, Austin, TX, USA, January 2015.

D. Yang, D. Zhang, Z. Yu, and Z. Yu, “Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs”, Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp ’13), Association for Computing Machinery, New York, NY, USA, 479–488, 2013, doi: https://doi.org/10.1145/2493432.2493464.

M. H. Park, J. H. Hong, S.B. Cho, “Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices,” inIndulska J., Ma J., Yang L.T., Ungerer T., Cao J. (eds)Ubiquitous Intelligence and Computing. UIC 2007. Lecture Notes in Computer Science, 2007, vol 4611. Springer, Berlin, Heidelberg, doi: https://doi.org/10.1007/978-3-540-73549-6_110.

B. Hu, and M. Ester, “Spatial topic modeling in online social media for location recommendation,” in RecSys ‘13: Proceedings of the 7th ACM conference on Recommender systems, pp. 25-32.

H. Yin, Y. Sun, B. Cui, Z. Hu, and L. Chen, “LCARS: a location-content-aware recommender system,” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’13), Association for Computing Machinery, New York, NY, USA, 221–229, 2013, doi: https://doi.org/10.1145/2487575.2487608.

B. Liu, and H. Xiong, “Point-of-interest recommendation in location based social networks with topic and location awareness,” Proceedings of the 2013 SIAM international conference on data mining, pp. 396-404, May 2013, Society for Industrial and Applied Mathematics.

D. Yang, D. Zhang, Z. Yu and Z. Wang, “A sentiment-enhanced personalized location recommendation system,” Proceedings of the 24th ACM conference on hypertext and social media, pp. 119-128, 2013.

B. Hu and M. Ester, “Social Topic Modeling for Point-of-Interest Recommendation in Location-Based Social Networks,” 2014 IEEE International Conference on Data Mining, Shenzhen, 2014, pp. 845-850, doi: 10.1109/ICDM.2014.124.

Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. Magnenat- Thalmann, “Time-aware point-of-interest recommendation” Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’13), Association for Computing Machinery, New York, NY, USA, 363–372, 2013, doi: https://doi.org/10.1145/2484028.2484030.

J S. Breese, D. Heckerman, C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA, 98052, 1998.

M. Ye, P. Yin, W. C. Lee and D. L. Lee, “Exploiting geographical influence for collaborative point-of-interest recommendation,” Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR ’11), Association for Computing Machinery, New York, NY, USA, 325–334, 2011, doi: https://doi.org/10.1145/2009916.2009962.

J. J. Levandoski, M. Sarwat, A. Eldawy and M. F. Mokbel, “LARS: A Location-Aware Recommender System,” 2012 IEEE 28th International Conference on Data Engineering, Washington, DC, 2012, pp. 450-461, doi: 10.1109/ICDE.2012.54.

T. Hofmann, and J. Puzicha, “Latent class models for collaborative filtering,” Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI , vol. 2, pp. 688-693.

K. Miyahara and M. J. Pazzani, “Collaborative filtering with the simple bayesian classifier”, in Pacific Rim International Conference on Artificial Intelligence, pp. 679-689, 2000, Springer, Berlin, Heidelberg.

Y. Liu, W. Wei, A. Sun, and C. Miao, “Exploiting Geographical Neighborhood Characteristics for Location Recommendation,” in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM ’14). Association for Computing Machinery, New York, NY, USA, 739–748, doi: https://doi.org/10.1145/2661829.2662002.

J. Zhu, C. Wang, X. Guo, Q. Ming, J. Li, and Y. Liu, “Friend and POI recommendation based on social trust cluster in location-based social networks”, EURASIP Journal on Wireless Communications and Networking, 2019, no. 1, 89.

Y. Koren, R. Bell and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” in Computer, vol. 42, no. 8, pp. 30-37, Aug. 2009, doi: 10.1109/MC.2009.263.

R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization,” Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07), Curran Associates Inc., Red Hook, NY, USA, 1257–1264, 2007.

D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Advances in Neural Information Processing Systems 13, pp. 556-562, 2001, Denver, CO, United States.

C. Cheng, H. Yang, I. King, and M. R. Lyu, “Fused matrix factorization with geographical and social influence in location-based social networks,” Proceedings of Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), pp. 17–23, Toronto, ON, Canada, July 2012.

S. Zhao, I. King and M. R. Lyu, “Capturing geographical influence in POI recommendations,” in International Conference on Neural Information Processing, pp. 530-537,2013, Springer, Berlin, Heidelberg.

N. D. Thang, C. Lihui and C. C. Keong, “An outlier-aware data clustering algorithm in mixture models,” 2009 7th International Conference on Information, Communications and Signal Processing (ICICS), Macau, 2009, pp. 1-5, doi: 10.1109/ICICS.2009.5397571.

D. Lian., C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui, “GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation,” Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining,pp. 831-840, August, 2014.

B. Liu, H. Xiong, S. Papadimitriou, Y. Fu and Z. Yao, “A General Geographical Probabilistic Factor Model for Point of Interest Recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 5, pp. 1167-1179, 1 May 2015, doi: 10.1109/TKDE.2014.2362525.

B. Sarwar, G. Karypis, J. Konstan and J. Riedl, “Application of dimensionality reduction in recommender system-a case study”, WebKDD-2000, Minnesota Univ Minneapolis Dept of Computer Science.

H. Polat and W. Du, “SVD-based collaborative filtering with privacy,” Proceedings of the 2005 ACM symposium on Applied computing,pp. 791-795.

H. Ma, H. Yang, M. R. Lyu, and I. King, “SoRec: social recommendation using probabilistic matrix factorization”, Proceedings of the 17th ACM conference on Information and knowledge management (CIKM ’08), Association for Computing Machinery, New York, NY, USA, 931–940, 2008, doi: https://doi.org/10.1145/1458082.1458205.

D. Cai, X. He, J. Han and T. S. Huang, “Graph Regularized Nonnegative Matrix Factorization for Data Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1548-1560, Aug. 2011, doi: 10.1109/TPAMI.2010.231.

P. Gopalan, L. Charlin, D.M. Blei, “Content-based recommendations with poisson factorization,” Advances in Neural Information Processing Systems 27: AnnualConference on Neural Information Processing Systems, December 2014, vol. 2, pp. 3176-3184, Montreal, Quebec, Canada.

P. Gopalan, J. M. Hofman, and D. M. Blei, “Scalable recommendation with poisson factorization,” arXiv preprint arXiv:1311.1704, 2013.

H. Ma, C. Liu, I. King, and M. R. Lyu, “Probabilistic factor models for web site recommendation,” Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR ’11), Association for Computing Machinery, New York, NY, USA, 265–274, July, 2011, doi: https://doi.org/10.1145/2009916.2009955.

Y. Liu, T. N. Pham, G. Cong, and Q. Yuan, “An experimental evaluation of point-of-interest recommendation in location-based social networks,” Proc. VLDB Endow., vol. 10, no. 10, pp. 1010–1021, June, 2017, doi: https://doi.org/10.14778/3115404.3115407.

T. Chai,. and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)?- Arguments against avoiding RMSE in the literature”, Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014, doi:10.5194/gmd-7-1247-2014.

Zhou, Fan, Ruiyang Yin, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Jin Wu, “Adversarial point-of-interest recommendation”, In The World Wide Web Conference, pp. 3462-34618, 2019.

H.A. Rahmani, M. Aliannejadi, M. Baratchi, F. Crestani, Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation, In: Jose J. et al. (eds) Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. Springer, Cham. https://doi.org/10.1007/978-3-030-45439-5_14, 2020.

Li, Huayu, Richang Hong, Zhiang Wu, and Yong Ge. “A spatial-temporal probabilistic matrix factorization model for point-of-interest recommendation.” Proceedings of the 2016 SIAM international conference on data mining, pp. 117-125. Society for Industrial and Applied Mathematics, 2016.

Eliezer de Souza,da Silva, H. Langseth, & H Ramampiaro, “Content-based social recommendation with poisson matrix factorization.”, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 530-546. Springer, Cham, 2017.

[1]

R. Behl and I. Kashyap, “Locus recommendation using probabilistic matrix factorization techniques”, ing. Solidar, vol. 17, no. 1, pp. 1–25, Jan. 2021, doi: 10.16925/2357-6014.2021.01.10.

Download Citation

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cession of rights and ethical commitment

As the author of the article, I declare that is an original unpublished work exclusively created by me, that it has not been submitted for simultaneous evaluation by another publication and that there is no impediment of any kind for concession of the rights provided for in this contract.

In this sense, I am committed to await the result of the evaluation by the journal Ingeniería Solidaría before considering its submission to another medium; in case the response by that publication is positive, additionally, I am committed to respond for any action involving claims, plagiarism or any other kind of claim that could be made by third parties.

At the same time, as the author or co-author, I declare that I am completely in agreement with the conditions presented in this work and that I cede all patrimonial rights, in other words, regarding reproduction, public communication, distribution, dissemination, transformation, making it available and all forms of exploitation of the work using any medium or procedure, during the term of the legal protection of the work and in every country in the world, to the Universidad Cooperativa de Colombia Press.

Issue

Vol. 17 No. 1 (2021)

Published

2021-01-11

Downloads

PDF

XML

How to Cite

[1]

R. Behl and I. Kashyap, “Locus recommendation using probabilistic matrix factorization techniques”, ing. Solidar, vol. 17, no. 1, pp. 1–25, Jan. 2021, doi: 10.16925/2357-6014.2021.01.10.

Download Citation

Metrics

File downloads

257

https://plu.mx/plum/a/?doi=10.16925/2357-6014.2021.01.10