[目的/意义]本研究对网络学术资源平台个性化推荐服务进行分析，为用户视角下个性化推荐的深入研 究提供了现实依据，为识别和弥补学术界与工业界之间的研究鸿沟提供参考。[方法/过程]研究采用内容分析法 对23个网络学术资源平台提供的个性化推荐服务进行两轮编码分析，选取个性化推荐的展示形式、推荐内容、推 荐解释、推荐时间节点、推荐类型作为分析指标。[结果/结论]①网络学术资源平台个性化推荐的展示形式可分为 高亮凸显式、非差异化式、导航索引式、弹出式、提示性网页跳转式，且高亮凸显式使用最多；②推荐内容多为科学 文献及其属性的超链接；③不同平台的推荐解释具有相似性，但详细程度存在差异；④推荐的时间节点多发生在 用户提交检索词之后、浏览文献详情页之时、用户下载过程之中；⑤88.89%的平台提供的推荐类型为静态推荐。 个性化推荐算法未能把用户当前的交互行为、用户对个性化推荐的需求和情境因素纳入推荐算法之中，可能是导 致用户无法感受到性能优异的个性化推荐服务的重要原因。

[Purpose/significance] This study analyzed the Personalized Recommendation Service(PRS) of the Online Academic Resource Platform(OARP), provides a realistic basis for the in-depth study of personalized recommendation from the user's perspective, and provides a reference for identifying and bridging the research gap between academia and industry. [Method/process] The content analysis is used to conduct two rounds of coding analysis on the PRS provided by 23 OARP, and the display form, recommended content, recommendation explanation, recommendation time node, and recommendation type are selected as analysis indicators. [Result/conclusion] ①The display forms of person? alized recommendation on the OARP can be divided into highlighting, non-differentiated, navigation index, pop-up, prompt web page jump, and highlighting are used more; ②Recommended content is hyperlinks of scientific literature and its attributes; ③Recommended explanations on different platforms are similar, but the level of detail is different; ④The recommended time node mostly occurs after the user submits the search term, during to browsing the document details page, during the downloading; ⑤88.89% of the recommendation types provided by the platform are static recommendations. The failure of the personalized recommendation algorithm to incorporate the user's current interaction behavior, the demand for personalized recommendation, and the contextual factors into the recommendation algorithm is an important reason why users cannot actually feel the PRS with excellent performance.