Recent studies have shown that recommendation systems commonly suffer from
popularity bias. Popularity bias refers to the problem that popular items
(i.e., frequently rated items) are recommended frequently while less popular
items are recommended rarely or not at all. Researchers adopted two approaches
to examining popularity bias: (i) from the users' perspective, by analyzing how
far a recommendation system deviates from user's expectations in receiving
popular items, and (ii) by analyzing the amount of exposure that long-tail
items receive, measured by overall catalog coverage and novelty. In this paper,
we examine the first point of view in the book domain, although the findings
may be applied to other domains as well. To this end, we analyze the well-known
Book-Crossing dataset and define three user groups based on their tendency
towards popular items (i.e., Niche, Diverse, Bestseller-focused). Further, we
evaluate the performance of nine state-of-the-art recommendation algorithms and
two baselines (i.e., Random, MostPop) from both the accuracy (e.g., NDCG,
Precision, Recall) and popularity bias perspectives. Our results indicate that
most state-of-the-art recommendation algorithms suffer from popularity bias in
the book domain, and fail to meet users' expectations with Niche and Diverse
tastes despite having a larger profile size. Conversely, Bestseller-focused
users are more likely to receive high-quality recommendations, both in terms of
fairness and personalization. Furthermore, our study shows a tradeoff between
personalization and unfairness of popularity bias in recommendation algorithms
for users belonging to the Diverse and Bestseller groups, that is, algorithms
with high capability of personalization suffer from the unfairness of
popularity bias.

本文研究了阅读领域中流行度偏见的问题，并发现大多数现有最先进的推荐算法都存在流行度偏见，不能满足用户的不同口味需求，而侧重畅销图书的用户则更容易获得高质量的推荐建议。同时，本研究表明，在涉及到偏好多样性的用户群体中，个性化能力强的算法会受到偏见的不公平对待。