Book ; Online: Sequential Nature of Recommender Systems Disrupts the Evaluation Process
2022
Abstract: Datasets are often generated in a sequential manner, where the previous samples and intermediate decisions or interventions affect subsequent samples. This is especially prominent in cases where there are significant human-AI interactions, such as in ... ...
Abstract | Datasets are often generated in a sequential manner, where the previous samples and intermediate decisions or interventions affect subsequent samples. This is especially prominent in cases where there are significant human-AI interactions, such as in recommender systems. To characterize the importance of this relationship across samples, we propose to use adversarial attacks on popular evaluation processes. We present sequence-aware boosting attacks and provide a lower bound on the amount of extra information that can be exploited from a confidential test set solely based on the order of the observed data. We use real and synthetic data to test our methods and show that the evaluation process on the MovieLense-100k dataset can be affected by $\sim1\%$ which is important when considering the close competition. Codes are publicly available. Comment: To Appear in Third International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2022) |
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Keywords | Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence |
Subject code | 006 |
Publishing date | 2022-05-26 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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