CoSEM: Contextual and Semantic Embedding for App Usage Prediction

TL;DR

This paper introduces CoSEM, a novel model that combines semantic and contextual embeddings to improve app usage prediction accuracy across diverse scenarios, outperforming existing methods on multiple datasets.

Contribution

The paper proposes CoSEM, a new model integrating semantic and contextual embeddings for app usage prediction, demonstrating superior performance over baseline models.

Findings

Outperforms baseline models on three real-world datasets.

Achieves MRR scores over 0.55, 0.57, and 0.86.

Attains Hit rate scores above 0.71, 0.75, and 0.95.

Abstract

App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This paper address this problem by developing a model called Contextual and Semantic Embedding model for App Usage Prediction (CoSEM) for app usage prediction that leverages integration of 1) semantic information embedding and 2) contextual information embedding based on historical app usage of individuals. Extensive experiments show that the combination of semantic information and history app usage information enables our model to outperform the baselines on three real-world datasets, achieving an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75, and 0.95, respectively.