2015/11/17 by Ivens Portugal, Portugal, Ivens, Paulo Alencar +3
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Software Engineering (cs.SE) #cs.IR #cs.SE
paper · pdf · doi:10.48550/arxiv.1511.05262
arxiv created 2016/02/24 · arxiv updated 2016/02/25
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.