Summary of Appformer: a Novel Framework For Mobile App Usage Prediction Leveraging Progressive Multi-modal Data Fusion and Feature Extraction, by Chuike Sun et al.
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction
by Chuike Sun, Junzhou Chen, Yue Zhao, Hao Han, Ruihai Jing, Guang Tan, Di Wu
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Appformer is a novel framework for predicting mobile applications that draws inspiration from Transformer-like architectures’ efficiency in processing sequential data through self-attention mechanisms. The framework combines a Multi-Modal Data Progressive Fusion Module with a sophisticated Feature Extraction Module, leveraging synergies between multi-modal data fusion and data mining techniques while maintaining user privacy. It optimizes Points of Interest (POIs) associated with base stations using comprehensive comparative experiments to identify the most effective clustering method. The refined inputs are seamlessly integrated into the initial phases of cross-modal data fusion, where temporal units are encoded via word embeddings and subsequently merged in later stages. The Feature Extraction Module employs Transformer-like architectures specialized for time series analysis, distilling comprehensive features that are fine-tuned from the fusion module’s outputs, guaranteeing a robust and efficient extraction process. Extensive experimental validation confirms Appformer’s effectiveness, attaining state-of-the-art (SOTA) metrics in mobile app usage prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Appformer is a new way to predict which apps people will use on their phones. It uses special computer programs called Transformers to help it make good predictions. The program combines different types of data, like what’s happening right now and what happened before, to figure out what app someone might want to use next. It also makes sure that people’s personal information stays private. Appformer is really good at making predictions – better than anyone else so far! This could be important for companies that make apps or phone manufacturers. |
Keywords
» Artificial intelligence » Clustering » Feature extraction » Multi modal » Self attention » Time series » Transformer
