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

页码：494 / 501

页数：8

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