Literature recommendation systems (LRS) assist readers in the discovery of relevant content from the overwhelming amount of literature available. Despite the widespread adoption of LRS, there is a lack of research on the user-perceived recommendation characteristics for fundamentally different approaches to content-based literature recommendation. To complement existing quantitative studies on literature recommendation, we present qualitative study results that report on users' perceptions for two contrasting recommendation classes: (1) link-based recommendation represented by the Co-Citation Proximity (CPA) approach, and (2) text-based recommendation represented by Lucene's MoreLikeThis (MLT) algorithm. The empirical data analyzed in our study with twenty users and a diverse set of 40 Wikipedia articles indicate a noticeable difference between text- and link-based recommendation generation approaches along several key dimensions. The text-based MLT method receives higher satisfaction ratings in terms of user-perceived similarity of recommended articles. In contrast, the CPA approach receives higher satisfaction scores in terms of diversity and serendipity of recommendations. We conclude that users of literature recommendation systems can benefit most from hybrid approaches that combine both link- and text-based approaches, where the user's information needs and preferences should control the weighting for the approaches used. The optimal weighting of multiple approaches used in a hybrid recommendation system is highly dependent on a user's shifting needs.

中文翻译：

---

用户对维基百科文章基于链接和基于文本的推荐偏好的定性评估

文献推荐系统 (LRS) 可帮助读者从大量可用文献中发现相关内容。尽管 LRS 被广泛采用，但对于基于内容的文献推荐的根本不同方法的用户感知推荐特征缺乏研究。为了补充现有的文献推荐定量研究，我们提出了定性研究结果，报告了用户对两种不同推荐类别的看法：（1）由 Co-Citation Proximity（CPA）方法表示的基于链接的推荐，以及（2）文本基于 Lucene 的 MoreLikeThis (MLT) 算法的推荐。在我们的研究中对 20 名用户和 40 篇维基百科文章进行分析的实证数据表明，基于文本和基于链接的推荐生成方法在几个关键维度上存在显着差异。基于文本的 MLT 方法在用户感知的推荐文章相似度方面获得更高的满意度。相比之下，CPA 方法在建议的多样性和偶然性方面获得了更高的满意度。我们得出结论，文献推荐系统的用户可以从结合基于链接和基于文本的方法的混合方法中受益最大，其中用户的信息需求和偏好应该控制所用方法的权重。混合推荐系统中使用的多种方法的最佳权重高度依赖于用户的