A person's interests exist as an internal state and are difficult to define. Since only external actions are observable, a proxy must be used that represents someone's interests. Techniques like collaborative filtering, behavioral targeting, and hashtag analysis implicitly model an individual's interests. I argue that these models are limited to shallow, temporary interests, which do not reflect people's deeper interests or passions. I propose an alternative model of interests that takes advantage of a user's social graph. The basic principle is that people only follow those that interest them, so the social graph is an effective and robust proxy for people's interests.
@article{arxiv.1410.0316,
title = {Using social network graph analysis for interest detection},
author = {Brian Lee Yung Rowe},
journal= {arXiv preprint arXiv:1410.0316},
year = {2014}
}