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      Requirements Engineering for General Recommender Systems

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          Abstract

          In requirements engineering for recommender systems, software engineers must identify the data that drives the recommendations. This is a labor-intensive task, which is error-prone and expensive. One possible solution to this problem is the adoption of automatic recommender system development approach based on a general recommender framework. One step towards the creation of such a framework is to determine the type of data used in recommender systems. In this paper, a systematic review has been conducted to identify the type of user and recommendation data items needed by a general recommender system. A user and item model is proposed, and some considerations about algorithm specific parameters are explained. A further goal is to study the impact of the fields of big data and Internet of things on the development of recommender systems.

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          Journal
          2015-11-16
          2016-02-24
          1511.05262

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          cs.SE cs.IR

          Software engineering, Information & Library science

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