Account-based recommenders in open discovery environments

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Abstract
Purpose: This paper aims to introduce a machine learning-based “My Account” recommender for implementation in open discovery environments such as VuFind among others. Design/methodology/approach: The approach to implementing machine learning-based personalized recommenders is undertaken as applied research leveraging data streams of transactional checkout data from discovery systems. Findings: The authors discuss the need for large data sets from which to build an algorithm and introduce a prototype recommender service, describing the prototype’s data flow pipeline and machine learning processes. Practical implications: The browse paradigm of discovery has neglected to leverage discovery system data to inform the development of personalized recommendations; with this paper, the authors show novel approaches to providing enhanced browse functionality by way of a user account. Originality/value: In the age of big data and machine learning, advances in deep learning technology and data stream processing make it possible to leverage discovery system data to inform the development of personalized recommendations.

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