ABSTRACT
The workshop features presentations of accepted contributions to the RecSys Challenge 2021, organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set is provided by Twitter. The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. For 2021, the challenge considers four different engagement types: Likes, Retweet, Quote, and replies. This year’s challenge brings the problem even closer to Twitter’s real recommender systems by introducing latency constraints. We also increases the data size to encourage novel methods. Also, the data density is increased in terms of the graph where users are considered to be nodes and interactions as edges. The goal is twofold: to predict the probability of different engagement types of a target user for a set of Tweets based on heterogeneous input data while providing fair recommendations. In fact, multi-goal optimization considering accuracy and fairness is particularly challenging. However, we believed that the recommendation community was nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations. To this end, Twitter has released a public dataset of close to 1 billion data points, > 40 million each day over 28 days. Week 1 − 3 will be used for training and week 4 for evaluation and testing. Each datapoint contains the tweet along with engagement features, user features, and tweet features. A peculiarity of this challenge is related to keeping the dataset updated with the platform: if a user deletes a Tweet, or their data from Twitter, the dataset is promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics. The challenge was well received with 578 registered users, and 386 submissions.
- Fabian Abel, Yashar Deldjoo, Mehdi Elahi, and Daniel Kohlsdorf. 2017. RecSys Challenge 2017: Offline and Online Evaluation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 372–373. https://doi.org/10.1145/3109859.3109954Google ScholarDigital Library
- Vito Walter Anelli, Pierpaolo Basile, Derek G. Bridge, Tommaso Di Noia, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Markus Zanker. 2018. Knowledge-aware and conversational recommender systems. In RecSys. ACM, 521–522.Google Scholar
- Vito Walter Anelli, Andrea Calì, Tommaso Di Noia, Matteo Palmonari, and Azzurra Ragone. 2016. Exposing Open Street Map in the Linked Data Cloud. In Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings(Lecture Notes in Computer Science, Vol. 9799), Hamido Fujita, Moonis Ali, Ali Selamat, Jun Sasaki, and Masaki Kurematsu (Eds.). Springer, 344–355. https://doi.org/10.1007/978-3-319-42007-3_29Google ScholarCross Ref
- Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, and Antonio Ferrara. 2019. Towards Effective Device-Aware Federated Learning. In AI*IA(Lecture Notes in Computer Science, Vol. 11946). Springer, 477–491.Google Scholar
- Vito Walter Anelli, Amra Delić, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, 2020. RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline. In Fourteenth ACM Conference on Recommender Systems. 623–627.Google Scholar
- Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, Gabriele Sottocornola, Walter Anelli, Nazareno Andrade, Jessie Smith, and Wenzhe Shi. 2020. Privacy-Aware Recommender Systems Challenge on Twitter’s Home Timeline. arxiv:2004.13715 [cs.SI]Google Scholar
- Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. 2018. Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 527–528. https://doi.org/10.1145/3240323.3240342Google ScholarDigital Library
- Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, and Tommaso Di Noia. 2020. A flexible framework for evaluating user and item fairness in recommender systems. User Modeling and User-Adapted Interaction(2020), 1–47.Google Scholar
- Amra Delic, Judith Masthoff, Julia Neidhardt, and Hannes Werthner. 2018. How to Use Social Relationships in Group Recommenders: Empirical Evidence. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP 2018, Singapore, July 08-11, 2018, Tanja Mitrovic, Jie Zhang, Li Chen, and David Chin(Eds.). ACM, 121–129. https://doi.org/10.1145/3209219.3209226Google ScholarDigital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arxiv:1810.04805 [cs.CL]Google Scholar
- Dylan Hadfield-Menell and Gillian K. Hadfield. 2019. Incomplete Contracting and AI Alignment. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 417–422. https://doi.org/10.1145/3306618.3314250Google ScholarDigital Library
- Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard-Paul Leyson, and Philipp Monreal. 2019. RecSys Challenge 2019: Session-Based Hotel Recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 570–571. https://doi.org/10.1145/3298689.3346974Google ScholarDigital Library
- Smitha Milli, Luca Belli, and Moritz Hardt. 2021. From Optimizing Engagement to Measuring Value. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21). Association for Computing Machinery, New York, NY, USA, 714–722. https://doi.org/10.1145/3442188.3445933Google ScholarDigital Library
- Arvind Narayanan and Vitaly Shmatikov. 2006. How To Break Anonymity of the Netflix Prize Dataset. arxiv:cs/0610105 [cs.CR]Google Scholar
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.Google ScholarDigital Library
- Pablo Sánchez and Alejandro Bellogín. 2018. Time-Aware Novelty Metrics for Recommender Systems. In Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings(Lecture Notes in Computer Science, Vol. 10772), Gabriella Pasi, Benjamin Piwowarski, Leif Azzopardi, and Allan Hanbury (Eds.). Springer, 357–370. https://doi.org/10.1007/978-3-319-76941-7_27Google Scholar
- Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19). Association for Computing Machinery, New York, NY, USA, 59–68. https://doi.org/10.1145/3287560.3287598Google ScholarDigital Library
- Latanya Sweeney. 1997. Guaranteeing anonymity when sharing medical data, the Datafly System. In Proceedings: a conference of the American Medical Informatics Association. AMIA Fall Symposium. Hanley & Belfus, Inc., Nashville, TN, USA, 51—55. https://europepmc.org/articles/PMC2233452Google Scholar
- Sergio Torrijos, Alejandro Bellogín, and Pablo Sánchez. 2020. Discovering Related Users in Location-based Social Networks. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, Genoa, Italy, July 12-18, 2020, Tsvi Kuflik, Ilaria Torre, Robin Burke, and Cristina Gena (Eds.). ACM, 353–357. https://doi.org/10.1145/3340631.3394882Google ScholarDigital Library
- Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17(2020), 261–272. https://doi.org/10.1038/s41592-019-0686-2Google Scholar
Recommendations
The 2021 RecSys Challenge Dataset: Fairness is not optional
RecSysChallenge '21: Proceedings of the Recommender Systems Challenge 2021After the success the RecSys 2020 Challenge, we are describing a novel and bigger dataset that was released in conjunction with the ACM RecSys Challenge 2021. This year’s dataset is not only bigger (~1B data points, a 5 fold increase), but for the first ...
RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline
RecSys '20: Proceedings of the 14th ACM Conference on Recommender SystemsThe workshop features presentations of accepted contributions to the RecSys Challenge 2020, organized by Politecnico di Bari, Free University of Bozen-Bolzano, TU Wien, University of Colorado, Boulder, and Universidade Federal de Campina Grande, and ...
An analysis of the 2014 RecSys Challenge
RecSysChallenge '14: Proceedings of the 2014 Recommender Systems ChallengeThe RecSys challenge 2014 focuses on the engagement generated by the tweets posted by the users of the IMDb application for smartphones. Such engagement depends on attributes concerning: the user who posts the message (e.g., his role in the social ...
Comments