Bibliometric‐enhanced legal information retrieval: Combining usage and citations as flavors of impact relevance
本文利用商业法律搜索引擎的数据，开发并评估了一种结合文档使用次数和引用次数的文献计量增强排序算法，该算法使法律专业人士的搜索会话时间平均缩短2%至3%。
Abstract Bibliometric‐enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data‐driven approach, this article describes the development of a bibliometric‐enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric‐enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known‐item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.