Paper Info
Title
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.
Year
Venue
Field
2015
arXiv: Software Engineering
Recommender system,World Wide Web,Computer science,Internet of Things,Requirements engineering,Software,Big data
DocType
Volume
Citations 
Journal
abs/1511.05262
0
PageRank 
References 
Authors
0.34
43
3
Name
Order
Citations
PageRank
Ivens Portugal101.69
Paulo S. C. Alencar239345.89
Donald D. Cowan358190.75