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

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.

Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local News Worldwide

Local news has become increasingly important in the news industry due to its various benefits. It offers local audiences information that helps them participate in their communities and interests. It also serves as a reliable source of factual reporting that can prevent misinformation. Moreover, it can influence national audiences as some local stories may have wider implications for politics, environment or crime. Hence, detecting the exact geolocation and impact scope of local news is crucial for news recommendation systems. There are two fundamental things required in this process, (1) classify whether an article belongs to local news, and (2) identify the geolocation of the article and its scope of influence to recommend it to appropriate users. In this paper, we focus on the second step and propose (1) an efficient approach to determine the location and radius of local news articles, (2) a method to reconcile the user's location with the article's location, and (3) a metric to evaluate the quality of the local news feed. We demonstrate that our technique is scalable and effective in serving hyperlocal news to users worldwide.