TAP with ease: a generic recommendation system for trigger-action programming based on multi-modal representation learning

Abstract

The escalating popularity of smart devices has given rise to an increasing trend wherein users leverage customized trigger-action programming (TAP) rules within the Internet of Things (IoT) to automate various aspects of their lives. This article addresses the challenge of effectively combining functions provided by many smart devices and online services by introducing a novel multi-modal representation learning model called TAP-TAG. This model integrates both textual and graph structures inherent in TAP rules, offering a holistic method to rule recommendation. TAP-TAG comprises two branches: the Knowledge Graph Embedding model, which projects triplets extracted from the TAP dataset into embeddings, and convolution neural networks that extract semantic features from the textual content of TAP rules. Extensive experiments are conducted on real-world TAP datasets to evaluate our model’s ability to recommend relevant rules based on user preferences. The experimental results show that TAP-TAG can outperform the state-of-the-art method by 5% in Precision@5, indicating that TAP-TAG is highly effective in providing accurate and diverse recommendations for TAP rules.