Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario
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
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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.
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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
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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
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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
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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
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Publicado
07/11/2022
Como Citar
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.
