| Full text | |
| Author(s): |
da Costa, Arthur
;
Fressato, Eduardo
;
Neto, Fernando
;
Manzato, Marcelo
;
Campello, Ricardo
;
ACM
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS); v. N/A, p. 2-pg., 2018-01-01. |
| Abstract | |
This paper presents a polished open-source Python-based recommender framework named Case Recommender, which provides a rich set of components from which developers can construct and evaluate customized recommender systems. It implements well-known and state-of-the-art algorithms in rating prediction and item recommendation scenarios. The main advantage of the Case Recommender is the possibility to integrate clustering and ensemble algorithms with recommendation engines, easing the development of more accurate and efficient approaches. (AU) | |
| FAPESP's process: | 16/20280-6 - Semantic Organization of Collaborative Users' Annotations Applied in Recommender Systems |
| Grantee: | Marcelo Garcia Manzato |
| Support Opportunities: | Regular Research Grants |