Requirements Engineering for General Recommender Systems
This addresses the problem of costly requirements engineering for software engineers in recommender system development, but it is incremental as it builds on existing frameworks.
The paper tackles the labor-intensive and error-prone task of identifying data for recommender systems by conducting a systematic review to determine user and recommendation data types needed for a general framework, proposing a user and item model and explaining algorithm parameters.
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.