Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario

  • Thiago Silva UFSJ
  • Nicollas Silva UFMG
  • Carlos Mito UFSJ
  • Adriano C. M. Pereira UFMG
  • Leonardo Rocha UFSJ

Resumo


Nowadays, instead of the traditional batch paradigm where the system trains and predicts a model at scheduled times, new Recommender Systems (RSs) have become interactive models. In this case, the RS should continually recommend the most relevant item(s), receive the user feedback(s), and constantly update itself as a sequential decision model. Thus, the literature has modeled each recommender as a Multi-Armed Bandit (MAB) problem to select new arms (items) at each iteration. However, despite recent advances, MAB models have not yet been studied in some classical scenarios, such as the points-of-interest (POIs) recommendation. For this reason, this work intends to fill this scientific gap, adapting classical MAB algorithms for this context. This process is performed through an interactive recommendation framework called iRec. iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. This framework contains several MAB state-of-the-art algorithms, a hyperparameter adjustment module, different evaluation metrics, different visual metaphors to present the results, and statistical validation. By instantiating and adapting iRec to our context, we can assess the quality of different interactive recommenders for the POI recommendation scenario.
Palavras-chave: POI Recommendation, Multi-Armed Bandit, Framework

Referências

Marc Abeille and Alessandro Lazaric. 2017. Linear thompson sampling revisited. In Artificial Intelligence and Statistics. PMLR, 176–184. https://doi.org/10.48550/arXiv.1611.06534

Gediminas Adomavicius and Alexander Tuzhilin. 2005. 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 17, 6(2005), 734–749. https://doi.org/10.1007/978-1-4899-7637-11

Xavier Amatriain and Justin Basilico. 2015. Recommender systems in industry: A netflix case study. In Recommender systems handbook. Springer, 385–419.

Xavier Amatriain and Justin Basilico. 2016. Past, present, and future of recommender systems: An industry perspective. In Proceedings of the 10th ACM conference on recommender systems. 211–214. https://doi.org/10.1145/2959100.2959144

Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, and Joseph Trotta. 2019. Local popularity and time in top-n recommendation. In European Conference on Information Retrieval. Springer, 861–868. https://doi.org/10.1007/978-3-030-15712-8_63

Peter Auer. 2002. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research 3, Nov (2002), 397–422. https://doi.org/10.1162/153244303321897663

Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2 (2002), 235–256. https://doi.org/10.1023/A:1013689704352

Alejandro Bellogín and Pablo Sánchez. 2017. Revisiting Neighbourhood-Based Recommenders For Temporal Scenarios.. In RecTemp RecSys. 40–44.

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender systems survey. Knowledge-based systems 46 (2013), 109–132. https://doi.org/10.1016/j.knosys.2013.03.012

Rodrigo Carvalho, Nícollas Silva, Luiz Chaves, Adriano C. M. Pereira, and Leonardo Rocha. 2019. Geographic-Categorical Diversification in POI Recommendations. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web (Rio de Janeiro, Brazil) (WebMedia ’19). Association for Computing Machinery, New York, NY, USA, 349–356. https://doi.org/10.1145/3323503.3349554

Olivier Chapelle and Lihong Li. 2011. An empirical evaluation of thompson sampling. Advances in neural information processing systems 24 (2011), 2249–2257.

Luiz Chaves, Nícollas Silva, Rodrigo Carvalho, Adriano C. M. Pereira, and Leonardo Rocha. 2019. Exploiting the User Activity-Level to Improve the Models’ Accuracy in Point-of-Interest Recommender Systems. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web (Rio de Janeiro, Brazil) (WebMedia ’19). Association for Computing Machinery, New York, NY, USA, 341–348. https://doi.org/10.1145/3323503.3349551

Ignacio Fernández-Tobías, Paolo Tomeo, Iván Cantador, Tommaso Di Noia, and Eugenio Di Sciascio. 2016. Accuracy and diversity in cross-domain recommendations for cold-start users with positive-only feedback. In Proceedings of the 10th ACM Conference on Recommender Systems. 119–122. https://doi.org/10.1145/2959100.2959175

Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the 7th ACM conference on Recommender systems. 93–100. https://doi.org/10.1145/3428658.3430970

Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems. 257–260. https://doi.org/10.1145/1864708.1864761

Steven Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao. 2018. Online Learning: A Comprehensive Survey. (02 2018). https://doi.org/10.1016/j.neucom.2021.04.112

Jaya Kawale, Hung H Bui, Branislav Kveton, Long Tran-Thanh, and Sanjay Chawla. 2015. Efficient thompson sampling for online matrix-factorization recommendation. In Advances in neural information processing systems.

Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web. 661–670. https://doi.org/10.1145/1772690.1772758

Shuai Li, Alexandros Karatzoglou, and Claudio Gentile. 2016. Collaborative filtering bandits. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 539–548. https://doi.org/10.1145/2911451.2911548

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. 433–442.

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. 831–840. https://doi.org/10.1145/2623330.2623638

Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 1043–1051. https://doi.org/10.1145/2487575.2487673

Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 739–748. https://doi.org/10.1145/2661829.2662002

Behrooz Omidvar-Tehrani, Sruthi Viswanathan, Frederic Roulland, and Jean-Michel Renders. 2020. SAGE: Interactive State-aware Point-of-Interest Recommendation. In WSDM Workshop SUM, Vol. 20. https://doi.org/10.1145/1122445.1122456

Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In Fourteenth ACM Conference on Recommender Systems. 240–248. https://doi.org/10.1145/3383313.3412488

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, 1–35. https://doi.org/10.1007/978-0-387-85820-3_1

Javier Sanz-Cruzado, Pablo Castells, and Esther López. 2019. A simple multi-armed nearest-neighbor bandit for interactive recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems. 358–362. https://doi.org/10.1145/3298689.3347040

Gunnar Schröder, Maik Thiele, and Wolfgang Lehner. 2011. Setting goals and choosing metrics for recommender system evaluations. In UCERSTI2 workshop at the 5th ACM conference on recommender systems, Chicago, USA, Vol. 23. 53.

Sulthana Shams, Daron Anderson, and Douglas Leith. 2021. Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users. (2021). https://doi.org/10.1145/3404835.3463033

Nícollas Silva, Heitor Werneck, Thiago Silva, Adriano C. M. Pereira, and Leonardo Rocha. 2021. A Contextual Approach to Improve the User’s Experience in Interactive Recommendation Systems. In Proceedings of the Brazilian Symposium on Multimedia and the Web (Belo Horizonte, Minas Gerais, Brazil) (WebMedia ’21). Association for Computing Machinery, New York, NY, USA, 89–96. https://doi.org/10.1145/3470482.3479621

Thiago Silva, Nícollas Silva, Heitor Werneck, Carlos Mito, Adriano CM Pereira, and Leonardo Rocha. 2022. iRec: An Interactive Recommendation Framework. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3165–3175. https://doi.org/10.1145/3477495.3531754

Thiago Silveira, Min Zhang, Xiao Lin, Yiqun Liu, and Shaoping Ma. 2019. How good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics 10, 5(2019), 813–831. https://doi.org/10.1007/s13042-017-0762-9

Richard S Sutton and Andrew G Barto. 1999. Reinforcement learning: An introduction. MIT press. https://doi.org/10.1017/S0263574799271172

Saúl Vargas and Pablo Castells. 2011. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems. 109–116. https://doi.org/10.1145/2043932.2043955

Dongjie Wang, Kunpeng Liu, Hui Xiong, and Yanjie Fu. 2022. Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams. arXiv preprint arXiv:2201.10983(2022). https://doi.org/10.48550/arXiv.2201.10983

Huazheng Wang, Qingyun Wu, and Hongning Wang. 2017. Factorization bandits for interactive recommendation. In Thirty-First AAAI Conference on Artificial Intelligence.

Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, S Sitharama Iyengar, Larisa Shwartz, and Genady Ya Grabarnik. 2018. Online interactive collaborative filtering using multi-armed bandit with dependent arms. IEEE Transactions on Knowledge and Data Engineering 31, 8(2018), 1569–1580. https://doi.org/10.1109/TKDE.2018.2866041

Yu-Xiong Wang and Martial Hebert. 2016. Learning to learn: Model regression networks for easy small sample learning. In European Conference on Computer Vision. Springer, 616–634. https://doi.org/10.1007/978-3-319-46466-4_37

Heitor Werneck, Rodrigo Santos, Nícollas Silva, Adriano C.M. Pereira, Fernando Mourão, and Leonardo Rocha. 2021. Effective and diverse POI recommendations through complementary diversification models. Expert Systems with Applications 175 (2021), 114775. https://doi.org/10.1016/j.eswa.2021.114775

Heitor Werneck, Nícollas Silva, Fernando Mourão, Adriano C. M. Pereira, and Leonardo Rocha. 2020. Combining Complementary Diversification Models for Personalized POI Recommendations. In Proceedings of the Brazilian Symposium on Multimedia and the Web (São Luís, Brazil) (WebMedia ’20). Association for Computing Machinery, New York, NY, USA, 209–212. https://doi.org/10.1145/3428658.3431754

Heitor Werneck, Nícollas Silva, Adriano Pereira, Matheus Carvalho, Alejandro Bellogín, Jorge Martinez-Gil, Fernando Mourão, and Leonardo Rocha. 2022. A reproducible POI recommendation framework: Works mapping and benchmark evaluation. Information Systems 108(2022), 102019. https://doi.org/10.1016/j.is.2022.102019

Heitor Werneck, Nícollas Silva, Matheus Carvalho Viana, Fernando Mourão, Adriano CM Pereira, and Leonardo Rocha. 2020. A survey on point-of-interest recommendation in location-based social networks. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 185–192.

Robert F Woolson. 2007. Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials(2007), 1–3. https://doi.org/10.1007/978-3-642-04898-2_616

Qingyun Wu, Naveen Iyer, and Hongning Wang. 2018. Learning contextual bandits in a non-stationary environment. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 495–504. https://doi.org/10.1145/3209978.3210051

Qingyun Wu, Huazheng Wang, Quanquan Gu, and Hongning Wang. 2016. Contextual bandits in a collaborative environment. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval.

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In The World Wide Web Conference. 2091–2102. https://doi.org/10.1145/3308558.3313442

Mao Ye, Peifeng Yin, and Wang-Chien Lee. 2010. Location recommendation for location-based social networks. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. 458–461. https://doi.org/10.11887/j.cn.201505001

Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 325–334. https://doi.org/10.1145/2009916.2009962

Zhengyi Yu, Youquan Wang, Jie Cao, and Guixiang Zhu. 2020. POI Recommendation with Interactive Behaviors and User Preference Dynamics Embedding. In 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 252–258. https://doi.org/10.1109/ICAIBD49809.2020.9137471

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. 363–372. https://doi.org/10.1145/2484028.2484030

ChengXiang Zhai, William W Cohen, and John Lafferty. 2015. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In ACM SIGIR Forum, Vol. 49. ACM New York, NY, USA, 2–9. https://doi.org/10.1145/860435.860440

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. 443–452. https://doi.org/10.1145/2766462.2767711

Xiaoxue Zhao, Weinan Zhang, and Jun Wang. 2013. Interactive collaborative filtering. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 1411–1420. https://doi.org/10.1145/2505515.2505690

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1059–1068. https://doi.org/10.1145/3219819.3219823

Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, and Yong Yu. 2020. Interactive recommender system via knowledge graph-enhanced reinforcement learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.48550/arXiv.2006.10389

Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, and Dawei Yin. 2020. Neural interactive collaborative filtering. In Proceedings of the 43rd International ACM SIGIR. 749–758. https://doi.org/10.48550/arXiv.2007.02095
Publicado
07/11/2022
SILVA, Thiago; SILVA, Nicollas; MITO, Carlos; PEREIRA, Adriano C. M.; ROCHA, Leonardo. Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 28. , 2022, Curitiba. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 225-235.

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