Behavioral Insights for Relationship Compatibility Using Digital Activity

DOI

10.2991/978-94-6463-866-0_68How to use a DOI?

Keywords

Digital Footprints; Behavioral Insights; Deep Learning; Graph Neural Networks (GNNs); Compatibility Prediction; Feature Extraction; Social Media Analysis; Natural Language Processing (NLP); Sentiment Analysis; User Engagement Patterns; Interaction Dynamics; Ethical AI; Data Privacy; Personalized Recommendations; Relationship Analytics

Abstract

As everything is going digital nowadays, online activity is a treasure trove of information to analyze and learn what humans love and how they behave. In this study, deep learning techniques are used to derive behavioral insights based on study questions to gauge relationship compatibility. Utilizing digital footprints like online browsing patterns, social media engagement, and content consumption characteristics we derive features that reflect personality, interests, and emotional propensities. We proposed framework embedded Deep learning module Graph Neural Networks, to predict compatibility score between individuals. We have a feature extraction pipeline with profile-based features (e.g., demographics, interests), as well as behavioral features (e.g., activity count, engagement force). Fixed feature extraction strategies extract static features from social network graph for characterizing interaction dynamics, and NLP techniques are applied into text content for extracting sentiments and topic agreements. It presents the ethical implications of data access and consent, which are necessary for development and responsible deployment. It lays the ground work for personalized and data-driven relationship platforms that yield valuable insights for developing compatibility in both personal and business relationships.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Manikandan
AU  - A. Rajalakshmi
PY  - 2025
DA  - 2025/10/31
TI  - Behavioral Insights for Relationship Compatibility Using Digital Activity
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 835
EP  - 848
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_68
DO  - 10.2991/978-94-6463-866-0_68
ID  - Manikandan2025
ER  -