%0 Conference Paper %1 Wang_2021 %A Wang, Ningxia %A Chen, Li %B Fifteenth ACM Conference on Recommender Systems %D 2021 %I ACM %K bias individual-differences recommender %P 133-142 %R 10.1145/3460231.3474244 %T User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms %U https://doi.org/10.1145%2F3460231.3474244 %X There are various biases in recommender systems. Recognizing biases, as well as unfairness caused by problematic biases, is the first step of system optimization. Related studies on algorithmic biases are mainly from the perspective of either items or users. For the latter (we call it “algorithmic user bias”), existing works have considered algorithms’ accuracy performances measured by accuracy metrics like RMSE. However, algorithmic user biases in beyondaccuracy measurements have rarely been studied, even though beyond-accuracy oriented recommendation algorithms have been increasingly investigated, with the purpose of breaking through the personalization limits of traditional accuracy-oriented algorithms (such as the typical “filter bubble” phenomenon). To fill in the research gap, in this work, we employ a large-scale survey dataset collected from a commercial platform, in which more than 11,000 users’ ratings on the recommendation’s 5 performance objectives (i.e., relevance, diversity, novelty, unexpectedness, and serendipity) and 8 kinds of user characteristics (i.e., gender, age, big-5 personality traits, and curiosity) are available. We study user biases of four algorithms (i.e., HOT, Rel-CF, Nov-CF, and Ser-CF) in terms of those five measurements between user groups of the eight user characteristics. We further look into users’ behavior patterns like the preference of using more positive ratings, in order to interpret the observed biases. Finally, based on the observed algorithmic user bias and users’ behavior patterns, we analyze the possible factors leading to the biases and recognize problematic biases that may lead to unfairness

@inproceedings{Wang_2021, abstract = {There are various biases in recommender systems. Recognizing biases, as well as unfairness caused by problematic biases, is the first step of system optimization. Related studies on algorithmic biases are mainly from the perspective of either items or users. For the latter (we call it “algorithmic user bias”), existing works have considered algorithms’ accuracy performances measured by accuracy metrics like RMSE. However, algorithmic user biases in beyondaccuracy measurements have rarely been studied, even though beyond-accuracy oriented recommendation algorithms have been increasingly investigated, with the purpose of breaking through the personalization limits of traditional accuracy-oriented algorithms (such as the typical “filter bubble” phenomenon). To fill in the research gap, in this work, we employ a large-scale survey dataset collected from a commercial platform, in which more than 11,000 users’ ratings on the recommendation’s 5 performance objectives (i.e., relevance, diversity, novelty, unexpectedness, and serendipity) and 8 kinds of user characteristics (i.e., gender, age, big-5 personality traits, and curiosity) are available. We study user biases of four algorithms (i.e., HOT, Rel-CF, Nov-CF, and Ser-CF) in terms of those five measurements between user groups of the eight user characteristics. We further look into users’ behavior patterns like the preference of using more positive ratings, in order to interpret the observed biases. Finally, based on the observed algorithmic user bias and users’ behavior patterns, we analyze the possible factors leading to the biases and recognize problematic biases that may lead to unfairness}, added-at = {2021-09-29T03:37:51.000+0200}, author = {Wang, Ningxia and Chen, Li}, biburl = {https://www.bibsonomy.org/bibtex/2871a92e9ed9605d32971ece166904137/brusilovsky}, booktitle = {Fifteenth {ACM} Conference on Recommender Systems}, description = {User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms | Fifteenth ACM Conference on Recommender Systems}, doi = {10.1145/3460231.3474244}, interhash = {9106c2081a912c97ab03668bcd65ac2d}, intrahash = {871a92e9ed9605d32971ece166904137}, keywords = {bias individual-differences recommender}, month = sep, pages = {133-142}, publisher = {{ACM}}, timestamp = {2021-09-29T03:37:51.000+0200}, title = {User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms}, url = {https://doi.org/10.1145%2F3460231.3474244}, year = 2021 }