社会科学学术搜索中推荐策略的连续在线评估
Continuous Online Evaluation of Recommendation Strategies in Social Science Academic Search
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中文总结 AI 辅助
本研究在社会科学学术搜索引擎GESIS Search中，利用STELLA框架实现并比较多种推荐算法，发现语义相似度推荐更受用户偏好，不同信息类型下推荐性能存在差异，为学术搜索推荐的连续评估提供了见解。
中文摘要 AI 辅助
在学术搜索引擎中提供相关推荐是一项复杂任务，原因在于学科领域、信息类型和用户偏好的多样性。在本案例研究中，我们通过在GESIS Search（一个面向社会科学的领域专用搜索引擎，为研究人员提供研究数据、出版物、变量及测量工具的访问权限）中集成并评估一系列推荐系统，来应对这些挑战。为支持针对实际平台用户的多种推荐策略的连续实时评估，我们使用了STELLA评估框架。我们实现并比较了多种不同的算法，包括传统的词汇相似度、基于Transformer嵌入的语义文档相似度，以及基于历史用户会话点击路径的会话式推荐。我们的结果显示，用户更偏好基于语义相似度的推荐，其表现优于术语相似度和会话式方法。不过，推荐系统的性能在GESIS Search的不同类别中存在差异，这表明信息类型不同，信息搜寻行为也有所不同。总体而言，我们的研究为如何融入连续评估以开发更贴合学术搜索门户偏好的推荐系统提供了见解。
英文摘要
Delivering relevant recommendations in academic search engines is a complex task due to the diversity of subject areas, information types, and user preferences. In this case study, we address these challenges by integrating and evaluating a range of recommendation systems within GESIS Search - a domain-specific search engine for the social sciences that provides researchers with access to research data, publications, variables, and measurement instruments. To support continuous, real-time evaluation of multiple recommendation strategies with actual platform users, we utilize the STELLA evaluation framework. We implement and compare a diverse set of algorithms, including traditional lexical similarity, semantic document similarity by using transformer-based embeddings, and session-based recommendations based on click paths from historical user sessions. Our results show that users prefer recommendations based on semantic similarity, which outperformed term-similarity and session-based methods. However, the performance of recommenders varies across categories within GESIS Search, suggesting that information-seeking behavior differs by information type. Overall, our study provides insights into how continuous evaluation can be incorporated to develop recommendations that better align with the preferences in academic search portals.
