Case recommender: A flexible and extensible python framework for recommender systems

Arthur Da Costa; Eduardo Fressato; Fernando Neto; Marcelo Manzato; Ricardo Campello

doi:10.1145/3240323.3241611

Case recommender: A flexible and extensible python framework for recommender systems

Arthur Da Costa, Eduardo Fressato, Fernando Neto, Marcelo Manzato, Ricardo Campello

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	RecSys 2018 - 12th ACM Conference on Recommender Systems
Publisher	Association for Computing Machinery
Publication date	27. Sept 2018
Pages	494-495
ISBN (Electronic)	9781450359016
DOIs	https://doi.org/10.1145/3240323.3241611
Publication status	Published - 27. Sept 2018
Externally published	Yes
Event	12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada Duration: 2. Oct 2018 → 7. Oct 2018

Conference

Conference	12th ACM Conference on Recommender Systems, RecSys 2018
Country/Territory	Canada
City	Vancouver
Period	02/10/2018 → 07/10/2018
Sponsor	ACM Special Interest Group Computer-Human Interaction (ACM SIGCHI)

Keywords

Framework
Python
Recommender Systems

Documents & Links

10.1145/3240323.3241611

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Cite this

@inproceedings{a4842c5666024fd6847e91daf5569217,

title = "Case recommender: A flexible and extensible python framework for recommender systems",

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.",

keywords = "Framework, Python, Recommender Systems",

author = "{Da Costa}, Arthur and Eduardo Fressato and Fernando Neto and Marcelo Manzato and Ricardo Campello",

note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 12th ACM Conference on Recommender Systems, RecSys 2018 ; Conference date: 02-10-2018 Through 07-10-2018",

year = "2018",

month = sep,

day = "27",

doi = "10.1145/3240323.3241611",

language = "English",

pages = "494--495",

booktitle = "RecSys 2018 - 12th ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery",

address = "United States",

}

Da Costa, A, Fressato, E, Neto, F, Manzato, M & Campello, R 2018, Case recommender: A flexible and extensible python framework for recommender systems. in RecSys 2018 - 12th ACM Conference on Recommender Systems. Association for Computing Machinery, pp. 494-495, 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, Canada, 02/10/2018. https://doi.org/10.1145/3240323.3241611

Case recommender: A flexible and extensible python framework for recommender systems. / Da Costa, Arthur; Fressato, Eduardo; Neto, Fernando et al.
RecSys 2018 - 12th ACM Conference on Recommender Systems. Association for Computing Machinery, 2018. p. 494-495.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Case recommender

T2 - 12th ACM Conference on Recommender Systems, RecSys 2018

AU - Da Costa, Arthur

AU - Fressato, Eduardo

AU - Neto, Fernando

AU - Manzato, Marcelo

AU - Campello, Ricardo

PY - 2018/9/27

Y1 - 2018/9/27

N2 - 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.

AB - 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.

KW - Framework

KW - Python

KW - Recommender Systems

U2 - 10.1145/3240323.3241611

DO - 10.1145/3240323.3241611

M3 - Article in proceedings

AN - SCOPUS:85056762812

SP - 494

EP - 495

BT - RecSys 2018 - 12th ACM Conference on Recommender Systems

PB - Association for Computing Machinery

Y2 - 2 October 2018 through 7 October 2018

ER -

Case recommender: A flexible and extensible python framework for recommender systems

Abstract

Conference

Keywords

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