A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network

TL;DR

This paper introduces a novel framework that combines prompt engineering, user feedback, and self-attention networks to improve the ranking of content providers in recommendation systems, enhancing content quality and diversity.

Contribution

It presents a new approach integrating language model prompts and self-attention networks for ranking content providers using explicit feedback and content features.

Findings

Improved content recommendation quality and credibility

Enhanced diversity of recommended content

Effective ranking performance demonstrated in online experiments

Abstract

This paper addresses the problem of ranking Content Providers for Content Recommendation System. Content Providers are the sources of news and other types of content, such as lifestyle, travel, gardening. We propose a framework that leverages explicit user feedback, such as clicks and reactions, and content-based features, such as writing style and frequency of publishing, to rank Content Providers for a given topic. We also use language models to engineer prompts that help us create a ground truth dataset for the previous unsupervised ranking problem. Using this ground truth, we expand with a self-attention based network to train on Learning to Rank ListWise task. We evaluate our framework using online experiments and show that it can improve the quality, credibility, and diversity of the content recommended to users.