Abstract
The resource management of an application is an essential task in smartphones. Optimizing the application launch process results in a faster and more efficient system, directly impacting the user experience. Predicting the next application that will be used can orient the smartphone to address the system resources to the correct application, making the system more intelligent and efficient. Neural networks have been presenting outstanding results in the state-of-the-art for mapping large sequences of data, outperforming all previous classification and prediction models. A recurrent neural network (RNN) is an artificial neural network associated with sequence models, and it can recognize patterns in sequences. One of the areas that use RNN is language modeling (LM). Given an arrangement of words, LM can learn how the words are organized in sentences, making it possible to predict the next word given a group of previous words. We propose building a predictive model inspired by LM. However, instead of using words, we will use previous applications to predict the next application. Moreover, some context features, such as timestamp and energy record, will be included in the prediction model to evaluate the impact of the features on the performance. We will provide the following application prediction result and extend it to the top-k possible candidates for the next application.
Highlights
The recent and brief history of smartphones is mainly marked by rapid technological growth.Both hardware and software have significantly improved in recent years, enabling and motivating the development of new applications
Natural App Processing (NAP) proposed in this work shows a better model to describe the application usage
We proposed analyzing in detail month by month predictions to better understand the app prediction task’s complexity
Summary
The recent and brief history of smartphones is mainly marked by rapid technological growth. Searching for an app becomes not practical, making the user spend a lot of time until finding the app This motivated researchers to develop app prediction methods, such as FALCON [3], which are based on context signals and user access patterns. Predict a company’s share price given its history on the stock market, or perform a weather forecast given the previous weather conditions [5] These examples are in the time-series prediction group, which means the dataset is following a timeline sequence. Another example, where RNN is often used is in language modeling (LM). We evaluate our model and compare it with other approaches, and we finalize with the conclusions
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