RecSys: An Unusual List of Recommended Reading

• Ji Y, Sun A, Zhang J and Li C (2023), "A Critical Study on Data Leakage in Recommender System Offline Evaluation", ACM Trans. Inf. Syst.. New York, NY, USA, February, 2023. Vol. 41(3) Association for Computing Machinery. [DOI]
• Deffayet R, Thonet T, Renders J-M and de Rijke M (2023), "Offline Evaluation for Reinforcement Learning-Based Recommendation: A Critical Issue and Some Alternatives", SIGIR Forum. New York, NY, USA, January, 2023. Vol. 56(2) Association for Computing Machinery. [DOI]
• Zhao WX, Lin Z, Feng Z, Wang P and Wen J-R (2022), "A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation Algorithms", ACM Trans. Inf. Syst.., May, 2022.
• Kleinberg J, Mullainathan S and Raghavan M (2022), "The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization", In Proceedings of the 23rd ACM Conference on Economics and Computation. Boulder, CO, USA , pp. 29. Association for Computing Machinery. [DOI]
• Verachtert R, Michiels L and Goethals B (2022), "Are We Forgetting Something? Correctly Evaluate a Recommender System With an Optimal Training Window", In Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES) at RecSys22. Seattle, WA, USA.
• Latifi S and Jannach D (2022), "Streaming Session-Based Recommendation: When Graph Neural Networks Meet the Neighborhood", In Proceedings of the 16th ACM Conference on Recommender Systems. New York, NY, USA , pp. 420-426. Association for Computing Machinery. [DOI]
• Li Y, Hedia M-L, Ma W, Lu H, Zhang M, Liu Y and Ma S (2022), "Contextualized Fairness for Recommender Systems in Premium Scenarios", Big Data Research. Vol. 27, pp. 100300. [DOI]
• Kolesnikov S and Andronov M (2022), "CVTT: Cross-Validation Through Time", CoRR. Vol. abs/2205.05393, pp. 1-12.
• Sysko-Romańczuk S, Zaborek P, Wróblewska A, Dąbrowski J and Tkachuk S (2022), "Data modalities, consumer attributes and recommendation performance in the fashion industry", Electronic Markets., August, 2022. [DOI]
• Ji Y, Sun A, Zhang J and Li C (2022), "Do Loyal Users Enjoy Better Recommendations? Understanding Recommender Accuracy from a Time Perspective", In Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval. New York, NY, USA , pp. 92-97. Association for Computing Machinery. [DOI]
• Jameson A, Willemsen MC and Felfernig A (2022), "Individual and Group Decision Making and Recommender Systems", In Recommender Systems Handbook. New York, NY , pp. 789-832. Springer US. [DOI]
• Castells P and Moffat A (2022), "Offline recommender system evaluation: Challenges and new directions", AI Magazine. Vol. 43(2), pp. 225-238. [DOI]
• Ferrari Dacrema M, Boglio S, Cremonesi P and Jannach D (2021), "A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research", ACM Trans. Inf. Syst.., January, 2021. Vol. 39(2) ACM. [DOI]
• Sánchez P and Bellogín A (2021), "On the effects of aggregation strategies for different groups of users in venue recommendation", Information Processing & Management. Vol. 58(5), pp. 102609. [DOI]
• Cremonesi P and Jannach D (2021), "Progress in Recommender Systems Research: Crisis? What Crisis?", AI Magazine., November, 2021. Vol. 42(3), pp. 43-54. [DOI]
• Tamm Y-M, Damdinov R and Vasilev A (2021), "Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?", In Proceedings of the 15th ACM Conference on Recommender Systems. New York, NY, USA , pp. 708-713. Association for Computing Machinery. [DOI]
• Woolridge D, Wilner S and Glick M (2021), "Sequence or Pseudo-Sequence? An Analysis of Sequential Recommendation Datasets", In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop co-located RecSys. Vol. 2955 CEUR-WS.org.
• Ji Y, Sun A, Zhang J and Li C (2020), "A Re-Visit of the Popularity Baseline in Recommender Systems", In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA , pp. 1749-1752. Association for Computing Machinery. [DOI]
• Sun Z, Yu D, Fang H, Yang J, Qu X, Zhang J and Geng C (2020), "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison", In Proceedings of the 14th ACM Conference on Recommender Systems. New York, NY, USA , pp. 23-32. Association for Computing Machinery. [DOI]
• Ferrari Dacrema M, Cremonesi P and Jannach D (2019), "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches", In Proceedings of the 13th ACM Conference on Recommender Systems. New York, NY, USA , pp. 101-109. Association for Computing Machinery. [DOI]
• Chen H, Chung C, Huang H and Tsui W (2017), "Common Pitfalls in Training and Evaluating Recommender Systems", SIGKDD Explorations. Vol. 19(1), pp. 37-45.