Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis
Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis
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DOI：
10.1145/3372923.3404793
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Kun-hsien Lin;Nasim Sonboli;B. Mobasher;R. Burke
中科院分区：
文献类型：
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作者：
Kun-hsien Lin;Nasim Sonboli;B. Mobasher;R. Burke
推荐系统从过去的用户偏好中学习，以预测未来的用户兴趣并为用户提供个性化建议。先前的研究表明，用户个人资料的总体偏差会影响对不具有多数偏好的用户的推荐。这种偏差传播效应的一个后果是校准错误，即用户喜欢的项目类型或类别与推荐中提供的项目之间的不匹配。在本文中，我们进行了系统分析，旨在识别用户档案中可能导致推荐错误的关键特征。我们考虑几类个人资料特征，包括与普通用户的相似性、受欢迎程度、个人资料多样性和偏好强度。我们开发了错误校准的预测模型，并在给定不同的算法和数据集特征的情况下，使用这些模型来识别与错误校准相关的最重要的特征。我们的分析旨在帮助系统设计人员预测校准错误的影响，并开发具有改进校准特性的推荐算法。
Recommender systems learn from past user preferences in order to predict future user interests and provide users with personalized suggestions. Previous research has demonstrated that biases in user profiles in the aggregate can influence the recommendations to users who do not share the majority preference. One consequence of this bias propagation effect is miscalibration, a mismatch between the types or categories of items that a user prefers and the items provided in recommendations. In this paper, we conduct a systematic analysis aimed at identifying key characteristics in user profiles that might lead to miscalibrated recommendations. We consider several categories of profile characteristics, including similarity to the average user, propensity towards popularity, profile diversity, and preference intensity. We develop predictive models of miscalibration and use these models to identify the most important features correlated with miscalibration, given different algorithms and dataset characteristics. Our analysis is intended to help system designers predict miscalibration effects and to develop recommendation algorithms with improved calibration properties.