From Netnews to Ethics - A Historical Overview of Recommender Systems

Abstract

In this talk I give a historical overview of recommender systems. Starting from the modern definition of recommender system in the early 1990 till today’s advanced recommendations methods and applications. The presentation bridges recommender systems to related fields, such as information retrieval, information systems, cognitive science, psychology, and machine learning.

Resources used in this talk:

1. Fix and Hodges. Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties. (1951)
2. Cover and Hart. Nearest neighbor pattern classification. (1967)
3. Karlgren. An Algebra for Recommendations. (1990)
4. Goldberg, Nichols, Oki, and Terry. Using collaborative filtering to weave an information tapestry. (1992)
5. Resnick, Iacovou, Suchak, Bergstrom, and Riedl. GroupLens: an open architecture for collaborative filtering of netnews. (1994)
6. Sarwar, Karypis, Konstan, and Riedl. Item-based collaborative filtering recommendation algorithms. (2001)
7. Funk. Netflix Update: Try This at Home. (2006)
8. Koren. Collaborative filtering with temporal dynamics. (2009)
9. Koren, Bell, Volinsky. Matrix Factorization Techniques for Recommender Systems. (2009)
10. Amatriain. Recommender Systems: We’re doing it (all) wrong. (2011)
11. Rajaraman. Five Stars Dominate Ratings. (2009)
12. Covington, Adams, and Sargin. Deep Neural Networks for YouTube Recommendations. (2016)
13. Hidasi, Karatzoglou, Baltrunas, and Tikk. Session-based Recommendations with Recurrent Neural Networks. (2016)
14. Liu, Zou, Zou, Wang, Zhang, Tang, Zhu, Zhu, Wu, Wang, Cheng. Monolith: Real Time Recommendation System With Collisionless Embedding Table. (2022)
15. Wu, Zheng, Qiu, Wang, Gu, Shen, Qin, Zhu, Zhu, Liu, Xiong, Chen. A Survey on Large Language Models for Recommendation. (2023)
16. Zhao, Fan, Li, Liu, Mei, Wang, Wen, Wang, Zhao, Tang, Li. Recommender Systems in the Era of Large Language Models (LLMs). (2023)
17. Liu, Liu, Zhou, Ye, Chong, Zhou, Xie, Cao, Wang, You, Yu. LLMRec: Benchmarking Large Language Models on Recommendation Task. (2023)
18. Deldjoo, He, McAuley, Korikov, Sanner, Ramisa, Vidal, Sathiamoorthy, Kasrizadeh, Milano, Ricci. Recommendation with Generative Models. (2024)
19. Ferrari Dacrema, Cremonesi, and Jannach. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. (2019)
20. Said and Bellogín. Comparative recommender system evaluation: benchmarking recommendation frameworks. (2014)
21. Armstrong, Moffat, Webber, and Zobel. Improvements that don’t add up: ad-hoc retrieval results since 1998. (2009)