| Title |
Influencing web customers. Deep learning practical application / |
| Authors |
Budnikas, Germanas ; Marculanas, Lukas |
| DOI |
10.1016/j.procs.2020.09.097 |
| Full Text |
|
| Is Part of |
Procedia computer science: Knowledge-based and intelligent information & engineering systems: proceedings of the 24th international conference KES2020.. Elsevier. 2020, vol. 176, p. 1015-1022.. ISSN 1877-0509 |
| Keywords [eng] |
machine learning ; recurrent neural networks ; stratified cross-validation ; user behavior ; class imbalance problem |
| Abstract [eng] |
With the global expansion of internet businesses, companies look for possibilities to increase their turnovers in virtual media. In these circumstances, it is significant for companies to employ online user behavior data in order to understand user intents and, to some extent, influence user online activity. This research examines the practical solution of the stated goal through an application of deep learning models. The contribution of the current study is an investigation of the success of real-time influence on web portal users to conduct specific activities leading to the achievement of predefined business goals. The study investigates several top-rated over-sampling techniques while solving class imbalance problem to evaluate their impact on the deep learning model performance. The study was conducted on the Lithuanian online educational platform. The obtained results indicate the feasibility of the suggested method. |
| Published |
Elsevier |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2020 |
| CC license |
|
