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From Netnews to Neural Nets: A History of Recommender Systems

From Netnews to Neural Nets: A History of Recommender Systems

Speaker: Alan Said – Gothenburg, Sweden
Topic(s): Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

Recommender systems have evolved dramatically over the past few decades, transitioning from early Netnews experiments to today’s neural network-driven and generative models. This talk presents a historical overview of the field, highlighting key shifts in algorithms, goals, and evaluation practices. From user-based collaborative filtering and rating prediction to deep learning and large language models, each generation of recommender systems has brought new capabilities—and new challenges. The talk also examines how concerns around fairness, transparency, and accountability have become central to the discourse. Understanding this evolution provides valuable context for shaping the next generation of responsible and human-centered recommender technologies.

About this Lecture

Number of Slides: 60 - 100
Duration: 45 - 100 minutes
Languages Available: English
Last Updated:

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