[Purpose/significance] In order to improve recommendation effects and the computation performance, a recommendation scheme is designed and its empirical study is realized in this paper based on the mass literature resource including bibliographic databases and various paper databases. [Method/process] Firstly, the paper analyzed the requirement of the literature recommendation of university libraries under the big data background. Then, it put forward the scheme and its algorithm implementation of library literature recommendation with the hybrid link strategy to solve the problems of the literature query deficit, the literature browse loss and the literature analysis inefficiency, and designed a case study based on in-memory computing technology of Spark. Finally, the paper discussed results of the experiment after comparison with similar algorithms. [Result/conclusion] The proposed scheme can meet the users' requirements efficiently and improve the performance and efficiency of literature recommendation and promote the application of big data in libraries at present.
He Sheng
,
Xiong Taichun
,
Liu Yijun
,
Ye Feiyue
,
Pan Yu
. A Spark-based Scheme of University Library Literature Recommendation and Its Empirical Study[J]. Library and Information Service, 2017
, 61(23)
: 129
-137
.
DOI: 10.13266/j.issn.0252-3116.2017.23.016
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