思想：从文章参考文献中选择文献进行推荐。
通过计算参考文献与当前文献的“相关性”也进行推荐。其中“相关性”定义涉及到了引用的次数，引用的方式等。
思考：
计算item-item之间的相关性（基于内容和基因某种结构和功能特性？），前提是有item对item的包含关系，且不同的item在这个包含关系里表现出的“重要性”不一样。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| BIBLME RecSys: Harnessing Bibliometric Measures for a Scholarly Paper Recommender System | |||||||||||
| 文献作者 | Anaı̈s Ollagnier, Sébastien Fournier, and Patrice Bellot | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| 【随意看】Recommender systems, Text mining, Digital libraries, Bib- liographic information, Bibliometrics. | |||||||||||
| 摘要描述 | |||||||||||
| The iterative continuum of scientific production generates a need for filtering and specific crossing of ideas and papers. In this paper, we present BIBLME RecSys software which is dedicated to the analysis of bibliographical references extracted from scientific collections of papers. Our goal is to provide users with paper suggestions guided by the papers they are reading and by the references they contain. To do so, we propose a new approach based on a new bibliometric measure. We propose to determine the impact, the inner representativeness, of each bibliographical reference according to their occurrences in the paper the user is reading. By means of this approach, we suggest central references of the author’s paper. As a result, we obtain papers that are related to the paper selected by the user according to the influence of references on it. We evaluate the recommendation in the context of a digital library dedicated to humanities and social sciences. | |||||||||||
