Vito Walter Anelli
Amra Delić
Gabriele Sottocornola
ABSTRACT
The 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 sponsored by Twitter. The challenge focuses on a real-world task of Tweet engagement prediction in a dynamic environment. The goal is to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with comment) of a target user for a set of Tweets, based on heterogeneous input data. To this end, Twitter has released a large public dataset of ~160M public Tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and Tweet features. A peculiarity of this challenge is related to the recent regulations on data protection and privacy. The challenge data set was compliant: if a user deleted a Tweet, or their data from Twitter, the dataset was 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 1,131 registered users. In the final phase, 20 teams were competing for the winning position. These teams had an average size of approximately 4 participants and developed an overall number of 127 different methods.
- 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 Scholar
Digital Library
- 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 and Tommaso Di Noia. 2019. 2nd Workshop on Knowledge-aware and Conversational Recommender Systems - KaRS. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019, Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 3001–3002. https://doi.org/10.1145/3357384.3358805Google ScholarDigital Library
- Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, and Joseph Trotta. 2019. Local Popularity and Time in top-N Recommendation. In Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14-18, 2019, Proceedings, Part I(Lecture Notes in Computer Science, Vol. 11437), Leif Azzopardi, Benno Stein, Norbert Fuhr, Philipp Mayr, Claudia Hauff, and Djoerd Hiemstra(Eds.). Springer, 861–868. https://doi.org/10.1007/978-3-030-15712-8_63Google Scholar
- Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, 2020. Privacy-Preserving Recommender Systems Challenge on Twitter’s Home Timeline. arXiv preprint arXiv:2004.13715(2020).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
- Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.Google ScholarDigital Library
- 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
- Chuong B Do, Quoc V Le, and Chuan-Sheng Foo. 2009. Proximal regularization for online and batch learning. In Proceedings of the 26th Annual International Conference on Machine Learning. 257–264.Google ScholarDigital Library
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in neural information processing systems. 3146–3154.Google Scholar
- 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
- Federico Monti, Michael Bronstein, and Xavier Bresson. 2017. Geometric matrix completion with recurrent multi-graph neural networks. In Proceedings of the 31st International Conference on Neural Information Processing Systems(Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 3700–3710.Google ScholarDigital Library
- Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: unbiased boosting with categorical features. In Advances in neural information processing systems. 6638–6648.Google Scholar
- 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
- Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108(2019).Google Scholar
- Gábor Takács and Domonkos Tikk. 2012. Alternating least squares for personalized ranking. In Proceedings of the sixth ACM conference on Recommender systems. 83–90.Google ScholarDigital Library
- 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
Recommendations
RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline
RecSys '21: Proceedings of the 15th ACM Conference on Recommender SystemsThe 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 ...
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 ...
Predicting User Engagement in Twitter with Collaborative Ranking
RecSysChallenge '14: Proceedings of the 2014 Recommender Systems ChallengeCollaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of items to the user. For instance, YouTube presents to a user a list of top-...
Comments