Iustina Ivanova
ABSTRACT
Outdoor sport climbing in Northern Italy attracts climbers from around the world. While this country has many rock formations, it offers enormous possibilities for adventurous people to explore the mountains. Unfortunately, this great potential causes a problem in finding suitable destinations (crags) to visit for climbing activity. Existing recommender systems in this domain address this issue and suggest potentially interesting items to climbers utilizing a content-based approach. These systems understand users’ preferences from past logs recorded in an electronic training diary. At the same time, some sports people have a behavioral tendency to revisit the same place for subjective reasons. It might be related to weather and seasonality (for instance, some crags are suitable for climbing in winter/summer only), the users’ preferences (when climbers like specific destinations more than others), or personal goals to be achieved in sport (when climbers plan to try some routes again). Unfortunately, current climbing crags recommendations do not adapt when users demonstrate these repetitive behavior patterns. Sequential recommender systems can capture such users’ habits since their architectures were designed to model users’ next item choice by learning from their previous decision manners. To understand to which extent these sequential recommendations can predict the following crags choices in sport climbing, we analyzed a scenario when climbers show repetitious decisions. Further, we present a data set from collected climbers’ e-logs in the Arco region (Italy) and applied several sequential recommender systems architectures for predicting climbers’ following crags’ visits from their past logs. We evaluated these recommender systems offline and compared ranking metrics with the other reported results on the different data sets. The work concludes that sequential models obtain comparably accurate results as in the studies conducted in the field of sequential recommender systems. Hence, it has the prospect for outdoor sport climbers’ subsequent visits prediction and recommendations.
Supplemental Material
- Ashton Anderson, Ravi Kumar, Andrew Tomkins, and Sergei Vassilvitskii. 2014. The Dynamics of Repeat Consumption. In Proceedings of the 23rd International Conference on World Wide Web (Seoul, Korea) (WWW ’14). Association for Computing Machinery, New York, NY, USA, 419–430. https://doi.org/10.1145/2566486.2568018Google Scholar
Digital Library
- Jimmy (Director) Chin and Elizabeth (Director) Chai Vasarhelyi. 2018. Free Solo. National Geographic Partners. Retrieved July 2, 2023 from https://www.imdb.com/title/tt7775622/ (movie).Google Scholar
- Xinyan Fan, Zheng Liu, Jianxun Lian, Wayne Xin Zhao, Xing Xie, and Ji-Rong Wen. 2021. Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21). Association for Computing Machinery, New York, NY, USA, 1733–1737. https://doi.org/10.1145/3404835.3462978Google ScholarDigital Library
- Ni Luh Wiwik Sri Rahayu Ginantra, Gita Widi Bhawika, Ahmad Zamsuri, Fadjar Budianto, and GS Achmad Daengs. 2020. Decision Support System in Recommending Climbing Tourism Destinations with Profile Matching Method. IOP Conference Series: Materials Science and Engineering 835, 1 (apr 2020), 012054. https://doi.org/10.1088/1757-899X/835/1/012054Google Scholar
- F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (dec 2015), 19 pages. https://doi.org/10.1145/2827872Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1511.06939Google Scholar
- Yupeng Hou, Binbin Hu, Zhiqiang Zhang, and Wayne Xin Zhao. 2022. CORE: Simple and Effective Session-Based Recommendation within Consistent Representation Space. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 1796–1801. https://doi.org/10.1145/3477495.3531955Google ScholarDigital Library
- Yinghui Huang, Hui Liu, Weiqing Li, Zichao Wang, Xiangen Hu, and Weijun Wang. 2020. Lifestyles in Amazon: Evidence from online reviews enhanced recommender system. International Journal of Market Research 62, 6 (2020), 689–706. https://doi.org/10.1177/1470785319844146 arXiv:https://doi.org/10.1177/1470785319844146Google ScholarCross Ref
- David H Hubel and Torsten N Wiesel. 1962. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of physiology 160, 1 (1962), 106. https://doi.org/10.1113/jphysiol.1962.sp006837Google ScholarCross Ref
- Iustina Ivanova. 2023. Predictive models for repetitive outdoor climbing crags recommendations. https://zenodo.org/badge/latestdoi/671457644. https://doi.org/10.5281/zenodo.8189260 Accessed: 27 July 2023.Google Scholar
- Iustina Ivanova, Marina Andrić, and Francesco Ricci. 2022. Content-Based Recommendations for Crags and Climbing Routes. In Information and Communication Technologies in Tourism 2022, Jason L. Stienmetz, Berta Ferrer-Rosell, and David Massimo (Eds.). Springer International Publishing, Cham, 369–381. https://doi.org/10.1007/978-3-030-94751-4_33Google Scholar
- Iustina Ivanova and Mike Wald. 2023. How Can We Model Climbers’ Future Visits from Their Past Records?. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (Limassol, Cyprus) (UMAP ’23 Adjunct). Association for Computing Machinery, New York, NY, USA, 60–65. https://doi.org/10.1145/3563359.3597408Google ScholarDigital Library
- Iustina Alekseevna Ivanova, Attaullah Buriro, and Francesco Ricci. 2022. Map and Content-Based Climbing Recommender System. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (Barcelona, Spain) (UMAP ’22 Adjunct). Association for Computing Machinery, New York, NY, USA, 41–45. https://doi.org/10.1145/3511047.3536416Google ScholarDigital Library
- Dietmar Jannach and Malte Ludewig. 2017. When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 306–310. https://doi.org/10.1145/3109859.3109872Google ScholarDigital Library
- Dietmar Jannach, Massimo Quadrana, and Paolo Cremonesi. 2022. Session-Based Recommender Systems. Springer US, New York, NY, 301–334. https://doi.org/10.1007/978-1-0716-2197-4_8Google Scholar
- Wang-Cheng Kang and Julian McAuley. 2018. Self-Attentive Sequential Recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). 197–206. https://doi.org/10.1109/ICDM.2018.00035Google Scholar
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 60, 6 (may 2017), 84–90. https://doi.org/10.1145/3065386Google ScholarDigital Library
- Josh (Director) Lowell and Peter (Director) Mortimer. 2017. The Dawn Wall. Red Bull Media House. Retrieved July 2, 2023 from https://www.imdb.com/title/tt7286916/ movie.Google Scholar
- Chen Ma, Peng Kang, and Xue Liu. 2019. Hierarchical Gating Networks for Sequential Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19). Association for Computing Machinery, New York, NY, USA, 825–833. https://doi.org/10.1145/3292500.3330984Google ScholarDigital Library
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-Based Recommendations on Styles and Substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (Santiago, Chile) (SIGIR ’15). Association for Computing Machinery, New York, NY, USA, 43–52. https://doi.org/10.1145/2766462.2767755Google ScholarDigital Library
- Mountain Project. 2021. Rock Climbing Guide. https://www.mountainproject.com/route-guide. Accessed: 20 October 2021.Google Scholar
- Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten de Rijke. 2019. RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence 33, 01 (Jul. 2019), 4806–4813. https://doi.org/10.1609/aaai.v33i01.33014806Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing Personalized Markov Chains for Next-Basket Recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW ’10). Association for Computing Machinery, New York, NY, USA, 811–820. https://doi.org/10.1145/1772690.1772773Google ScholarDigital Library
- J. Rushforth. 2014. The Dolomites: Rock Climbs and Via Ferrata. Rockfax. https://books.google.it/books?id=ia7OoAEACAAJGoogle Scholar
- Dean Scarff. 2020. Estimation of Climbing Route Difficulty using Whole-History Rating. (2020). arxiv:2001.05388 [stat.AP]Google Scholar
- Maria Stratigi, Xiaozhou Li, Kostas Stefanidis, and Zheying Zhang. 2019. Ratings vs. Reviews in Recommender Systems: A Case Study on the Amazon Movies Dataset. In New Trends inDatabases and Information Systems, Tatjana Welzer, Johann Eder, Vili Podgorelec, Robert Wrembel, Mirjana Ivanović, Johann Gamper, Mikolaj Morzy, Theodoros Tzouramanis, Jérôme Darmont, and Aida Kamisalic Latific (Eds.). Springer International Publishing, Cham, 68–76. https://doi.org/10.1007/978-3-030-30278-8_9Google Scholar
- Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19). Association for Computing Machinery, New York, NY, USA, 1441–1450. https://doi.org/10.1145/3357384.3357895Google ScholarDigital Library
- Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, and Xia Hu. 2021. Sparse-Interest Network for Sequential Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM ’21). Association for Computing Machinery, New York, NY, USA, 598–606. https://doi.org/10.1145/3437963.3441811Google ScholarDigital Library
- Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved Recurrent Neural Networks for Session-Based Recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (Boston, MA, USA) (DLRS 2016). Association for Computing Machinery, New York, NY, USA, 17–22. https://doi.org/10.1145/2988450.2988452Google ScholarDigital Library
- Kosetsu Tsukuda and Masataka Goto. 2020. Explainable Recommendation for Repeat Consumption. In Proceedings of the 14th ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys ’20). Association for Computing Machinery, New York, NY, USA, 462–467. https://doi.org/10.1145/3383313.3412230Google ScholarDigital Library
- Marc Wilkes and Krzysztof Janowicz. 2008. A graph-based alignment approach to similarity between climbing routes. In Proc. First International Workshop on Information Semantics and its Implications for Geographic Analysis (ISGA). Citeseer.Google Scholar
- Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 33, 01 (Jul. 2019), 346–353. https://doi.org/10.1609/aaai.v33i01.3301346Google ScholarDigital Library
- Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019. Graph Contextualized Self-Attention Network for Session-Based Recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (Macao, China) (IJCAI’19). AAAI Press, 3940–3946. https://doi.org/10.5555/3367471.3367589Google ScholarDigital Library
- Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, and Xiangnan He. 2019. A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM ’19). Association for Computing Machinery, New York, NY, USA, 582–590. https://doi.org/10.1145/3289600.3290975Google ScholarDigital Library
- Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20). Association for Computing Machinery, New York, NY, USA, 1893–1902. https://doi.org/10.1145/3340531.3411954Google ScholarDigital Library
Index Terms
- Climbing crags repetitive choices and recommendations
Recommendations
Fair sequential group recommendations
SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied ComputingRecommender systems have been incorporated in our everyday life; from music to health recommendations, recommender systems have enhanced the users' experience. At the same time, with the expansion of social media, it is now easier than ever to form ...
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data miningThe goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Are All Rejected Recommendations Equally Bad?: Towards Analysing Rejected Recommendations
UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and PersonalizationWhen evaluating algorithms that recommend a list of relevant items to a user, it is common to use metrics such as precision to measure the system accuracy. When computing precision, one computes the number of items that were selected by the user among ...
Comments