Requirements Engineering for General Recommender Systems
Abstrak
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.
Penulis (3)
Ivens Portugal
Paulo Alencar
Donald Cowan
Akses Cepat
- Tahun Terbit
- 2015
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓