Outgoing call recommendation using neural network

Personalized data collected from smartphones and similar devices reflect time dependency. Currently, research using such data as a basis for prediction of future data is ongoing. This suggests that not only is time dependency reflected in past data but also in newly produced data, and that time-dependent weights also are reflected therein. This paper analyzed the most prominent feature of personalized data, the call log. The random forest method was used to find highly correlated data between call data and new outgoing calls. This information was then learned through a neural network. Data were collected from 10 subjects, and 80% was learned, while 20% was used for prediction. The results showed that for six suggested numbers, the Hit Ratio was 77% while for nine suggested numbers, it was 90%. This indicates that when nine numbers are recommended, the probability that one of them will be called is high. “Recent call volume” and “changing data” showed a high correlation, and experiments were conducted so that time change could be adequately reflected. The problem of repetitions was addressed while maintaining the Hit Ratio.