Requirements Engineering for General Recommender Systems
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.
Cite
@article{arxiv.1511.05262,
title = {Requirements Engineering for General Recommender Systems},
author = {Ivens Portugal and Paulo Alencar and Donald Cowan},
journal= {arXiv preprint arXiv:1511.05262},
year = {2016}
}