Book-recommendation systems are increasingly common, from Amazon to public library interfaces. However, for archives and special collections, such automated assistance has been rare. This is partly due to the complexity of descriptions (finding aids describing whole collections) and partly due to the complexity of the collections themselves (what is this collection about and how is it related to another collection?). The American Philosophical Society Library is using circulation data collected through the collection-management software package, Aeon, to automate recommendations. In our system, which we’re calling PAL (People Also Liked), recommendations are offered in two ways: based on interests (“You’re interested in X, other people interested in X looked at these collections”) and on specific requests (“You’ve looked at Y, other people who looked at Y also looked that these collections”). This article will discuss the development of PAL and plans for the system. We will also discuss ongoing concerns and issues, how patron privacy is protected, and the possibility of generalizing beyond any specific software solution.

中文翻译：

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PAL：建立手稿推荐系统

从Amazon到公共图书馆的界面，图书推荐系统越来越普遍。但是，对于档案馆和特殊馆藏，这种自动协助很少见。这部分是由于描述的复杂性（描述整个集合的辅助工具），另一部分是由于这些集合本身的复杂性（该集合涉及什么以及它与另一个集合有什么关系？）。美国哲学会图书馆正在使用通过收集管理软件包Aeon收集的流通数据来自动执行推荐。在我们称为PAL（也喜欢人们）的系统中，建议以两种方式提供：基于兴趣（“您对X感兴趣，对X感兴趣的其他人查看了这些集合”）和特定要求（“您看过Y，其他看过Y的人也看过这些收藏”。本文将讨论PAL的开发和系统计划。我们还将讨论持续存在的问题和问题，如何保护顾客的隐私，以及推广超出任何特定软件解决方案的可能性。