A Novel Recommendation System for Next Feature in Software

被引：0
|
作者：
Prata, Victor R. [1 ]
Moreira, Ronaldo S. [2 ]
Cordeiro, Luan S. [1 ]
Maia, Atilla N. [1 ]
Martins, Alan R. [1 ]
Leao, Davi A. [1 ]
Cavalcante, C. H. L. [1 ]
Souza Junior, Amauri H. [1 ]
Rocha Neto, Ajalmar R. [1 ]
机构：
[1] Fed Inst Ceara, Fortaleza, Ceara, Brazil
[2] Fortes Tecnol, Rua Antonio Fortes 330, BR-60813460 Fortaleza, Ceara, Brazil
来源：
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2019, PT I | 2019年 / 11871卷
关键词：
Recommendation system; Independently Recurrent Neural Network; Sequential recommendation; Deep learning; Commercial applications;
D O I：
10.1007/978-3-030-33607-3_53
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Software that needs to fulfill many tasks requires a large number of components. Users of these software need a lot of time to find the desired functionality or follow a particular workflow. Recommendation systems can optimize a user's working time by recommending the next features he/she needs. Given that, we evaluate the use of three algorithms (Markov Chain, IndRNN, and LSTM) commonly applied in sequence recommendation/classification in a dataset that reflects the use of the accounting software from Fortes Tecnologia. We analyze the results under two aspects: accuracy for top-5 recommendations and training time. The results show that the IndRNN achieved the highest accuracy, while the Markov Chain reached the lowest training time.
引用
收藏
页码：494 / 501
页数：8
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