Abstract
Point of interest (POI) recommender systems for location-based social networks, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated to improving recommendation services in such systems by integrating different features (e.g., time or geographic location) that are assumed to have an impact on people’s choices for POIs. Yet, little effort has been made to incorporate or even understand the impact of weather on user decisions regarding certain POIs. In this paper, we contribute to this area of research by presenting the novel results of a study that aims to recommend POIs based on weather data. To this end, we have expanded the state-of-the-art Rank-GeoFM POI recommender algorithm to include additional weather-related features such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly improves the recommendation accuracy in comparison to the original method, but also outperforms its time-based variant. Furthermore, we investigate the magnitude of the impact of each feature on the recommendation quality. Our research clearly shows the need to study weather context in more detail in light of POI recommendation systems. This study is relevant for researchers working on recommender systems in general, but in particular for researchers and system engineers working on POI recommender systems in the tourism domain.
Similar content being viewed by others
References
Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th international conference on advances in geographic information systems, SIGSPATIAL ’12, New York, NY, USA. ACM, pp 199–208
Bao J, Zheng Y, Wilkie D, Mokbel M (2015) Recommendations in location-based social networks: a survey. Geoinformatica 19(3):525–565
Borras J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370–7389
Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010: 19th international conference on computational statisticsParis France, August 22–27, 2010 Keynote, invited and contributed papers. Physica-Verlag HD, Heidelberg, pp 177–186
Braunhofer M, Elahi M, Ge M, Ricci F, Schievenin T (2013a) STS: design of weather-aware mobile recommender systems in tourism. In: Proceedings of the first international workshop on intelligent user interfaces: artificial intelligence meets human computer interaction (AI*HCI 2013) a workshop of the XIII international conference of the Italian association for artificial intelligence (AI*IA 2013), Turin, Italy, December 4, 2013
Braunhofer M, Elahi M, Ricci F, Schievenin T (2013b) Context-aware points of interest suggestion with dynamic weather data management. In: Information and communication technologies in tourism. Springer, Cham, pp 87–100
Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: Proceedings of the AAAI, pp 17–23
Ference G, Ye M, Lee W-C (2013) Location recommendation for out-of-town users in location-based social networks. In: Proceedings of the 22nd ACM international conference on information & knowledge management, CIKM ’13, New York, NY, USA. ACM, pp 721–726
Fesenmaier DR, Kuflik T, Neidhardt J (2016) Rectour 2016: workshop on recommenders in tourism. In: Proceedings of the 10th ACM conference on recommender systems, RecSys ’16, New York, NY, USA. ACM, pp 417–418
Gantner Z, Rendle S, Freudenthaler C, Schmidt-Thieme L (2011) MyMediaLite: a free recommender system library. In: In Proceedings of RecSys ’11
Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on recommender systems, RecSys ’13, New York, NY, USA. ACM, pp 93–100
Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333
Hall MA (1999) Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato
Horanont T, Phithakkitnukoon S, Leong TW, Sekimoto Y, Shibasaki R (2013) Weather effects on the patterns of people’s everyday activities: a study using gps traces of mobile phone users. PloS One 8(12):e81153
Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of ICDM ’08. IEEE, pp 263–272
Kohyama T, Wallace JM (2016) Rainfall variations induced by the lunar gravitational atmospheric tide and their implications for the relationship between tropical rainfall and humidity. Geophys Res Lett 43(2):918–923 (2015GL067342)
Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’09, New York, NY, USA. ACM, pp 447–456
Li X, Cong G, Li X-L, Pham T-AN, Krishnaswamy S (2015) Rank-geofm: a ranking based geographical factorization method for point of interest recommendation. In: Proceedings of SIGIR ’15, New York, NY, USA. ACM, pp 433–442
Macedo AQ, Marinho LB, Santos RL (2015) Context-aware event recommendation in event-based social networks. In: Proceedings of the 9th ACM conference on recommender systems, RecSys ’15, New York, NY, USA. ACM, pp 123–130
Martin D, Alzua A, Lamsfus C (2011) A contextual geofencing mobile tourism service. Springer, Vienna, pp 191–202
Meehan K, Lunney T, Curran K, McCaughey A (2013) Context-aware intelligent recommendation system for tourism. In: Pervasive computing and communications workshops (PERCOM workshops), 2013 IEEE international conference on. IEEE, pp 328–331
Noulas A, Scellato S, Lathia N, Mascolo C (2012) A random walk around the city: New venue recommendation in location-based social networks. In: Privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international conference on social computing (SocialCom), pp 144–153
Nunes I, Marinho L (2014) A personalized geographic-based diffusion model for location recommendations in lbsn. In: Proceedings of the 2014 9th Latin American web congress, LA-WEB ’14, Washington, DC, USA. IEEE Computer Society, pp 59–67
Parra D, Sahebi S (2013) Recommender systems: sources of knowledge and evaluation metrics. In: Velsquez JD, Palade V, Jain LC (eds) Advanced techniques in web intelligence-2, volume 452 of Studies in computational intelligence. Springer, Berlin, pp 149–175
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW ’01. ACM, pp 285–295
Trattner C, Oberegger A, Eberhard L, Parra D, Marinho LB et al (2016) Understanding the impact of weather for poi recommendations. In: RecTour@ RecSys, pp 16–23
Wang Q, Taylor JE (2014) Quantifying human mobility perturbation and resilience in hurricane sandy. PLoS One 9(11):1–5
Wang Q, Taylor JE (2015) Resilience of human mobility under the influence of typhoons. Procedia Eng 118(Supplement C):942–949 (Defining the future of sustainability and resilience in design, engineering and construction)
Wang Q, Taylor JE (2016) Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS One 11(1):1–14
Yang D, Zhang D, Qu B (2016) Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Trans Intell Syst Technol 7(3):30
Ye M, Yin P, Lee W-C, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of SIGIR ’11. ACM, pp 325–334
Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) Lcars: a location-content-aware recommender system. In: Proceedings of KDD ’13. ACM, pp 221–229
Zheng Y, Mobasher B, Burke R (2014) Cslim: contextual slim recommendation algorithms. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 301–304
Acknowledgements
We thank the reviewers for their valuable comments. Furthermore, we would like to acknowledge Prof. Rodrygo L. T. Santos, who provided us with useful feedback to improve the model section. The authors Denis Parra and Leandro Marinho were supported by CONICYT (project FONDECYT 11150783) and the EU-BR BigSea project (MCTI/RNP 3rd Coordinated Call).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Trattner, C., Oberegger, A., Marinho, L. et al. Investigating the utility of the weather context for point of interest recommendations. Inf Technol Tourism 19, 117–150 (2018). https://doi.org/10.1007/s40558-017-0100-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40558-017-0100-9