How Does an Online Shop Decide What to Recommend?
In short: A calm look at how recommenders turn what people buy, rate and view into suggestions, from content profiles and collaborative filtering to neural networks, and why their tilt toward the popular is still studied.
A 1:24 short film in the series “From Fingers to Qubits”, narrated by intellect_voice_01.
Sources used in this film: Academia.edu (hosting the introductory chapter of the Recommender Systems Handbook), “Introduction to recommender systems handbook”; Springer (chapter 'Introduction to Recommender Systems Handbook' by Francesco Ricci, Lior Rokach and Bracha Shapira). The page text supplied is only the chapter's reference list, so the excerpts belo…, “Introduction to Recommender Systems Handbook”; Academia.edu (user-uploaded copy of an IEEE Internet Computing article; the 'Key takeaways' and 'FAQs' sections are AI-generated summaries added by the site), “Amazon.com recommendations item-to-item collaborative filtering - Intern et Computing, IEEE”; IEEE (IEEE Xplore; IEEE Internet Computing article by Amazon.com authors), “Amazon.com recommendations: item-to-item collaborative filtering”; IEEE (IEEE Xplore; article in IEEE Computer), “Matrix Factorization Techniques for Recommender Systems”; Springer Nature (journal: Neural Computing and Applications), “Revisiting recommender systems: an investigative survey - Neural Computing and Applications”; Academia.edu (hosting Su & Khoshgoftaar, 'A Survey of Collaborative Filtering Techniques', Advances in Artificial Intelligence, Hindawi, 2009, doi:10.1155/2009/421425), “A Survey of Collaborative Filtering Techniques”; University of Minnesota, College of Science and Engineering (Department of Computer Science & Engineering news), “GroupLens: A Human-Centered Approach to AI”; Springer Nature (User Modeling and User-Adapted Interaction journal), “A survey on popularity bias in recommender systems - User Modeling and User-Adapted Interaction”.
Sources used in this film
- Academia.edu (hosting the introductory chapter of the Recommender Systems Handbook) — Introduction to recommender systems handbook · View →
- Springer (chapter 'Introduction to Recommender Systems Handbook' by Francesco Ricci, Lior Rokach and Bracha Shapira). The page text supplied is only the chapter's reference list, so the excerpts belo… — Introduction to Recommender Systems Handbook · View →
- Academia.edu (user-uploaded copy of an IEEE Internet Computing article; the 'Key takeaways' and 'FAQs' sections are AI-generated summaries added by the site) — Amazon.com recommendations item-to-item collaborative filtering - Intern et Computing, IEEE · View →
- IEEE (IEEE Xplore; IEEE Internet Computing article by Amazon.com authors) — Amazon.com recommendations: item-to-item collaborative filtering · View →
- IEEE (IEEE Xplore; article in IEEE Computer) — Matrix Factorization Techniques for Recommender Systems · View →
- Springer Nature (journal: Neural Computing and Applications) — Revisiting recommender systems: an investigative survey - Neural Computing and Applications · View →
- Academia.edu (hosting Su & Khoshgoftaar, 'A Survey of Collaborative Filtering Techniques', Advances in Artificial Intelligence, Hindawi, 2009, doi:10.1155/2009/421425) — A Survey of Collaborative Filtering Techniques · View →
- University of Minnesota, College of Science and Engineering (Department of Computer Science & Engineering news) — GroupLens: A Human-Centered Approach to AI · View →
- Springer Nature (User Modeling and User-Adapted Interaction journal) — A survey on popularity bias in recommender systems - User Modeling and User-Adapted Interaction · View →
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Sources
- Summary by Intellect · source: Academia.edu (hosting the introductory chapter of the Recommender Systems Handbook): Introduction to recommender systems handbook
- Also: Springer (chapter 'Introduction to Recommender Systems Handbook' by Francesco Ricci, Lior Rokach and Bracha Shapira). Th: Introduction to Recommender Systems Handbook
- Also: Academia.edu (user-uploaded copy of an IEEE Internet Computing article; the 'Key takeaways' and 'FAQs' sections are AI-g: Amazon.com recommendations item-to-item collaborative filtering - Intern et Computing, IEEE
- Also: IEEE (IEEE Xplore abstract page): Amazon.com recommendations: item-to-item collaborative filtering
- Also: IEEE (IEEE Xplore; article published in IEEE's Computer magazine): Matrix Factorization Techniques for Recommender Systems
- Also: Springer Nature (journal: Neural Computing and Applications): Revisiting recommender systems: an investigative survey - Neural Computing and Applications
- Also: Academia.edu (hosting Su & Khoshgoftaar, 'A Survey of Collaborative Filtering Techniques', Advances in Artificial Intell: A Survey of Collaborative Filtering Techniques
- Also: University of Minnesota, College of Science and Engineering (Department of Computer Science & Engineering news): GroupLens: A Human-Centered Approach to AI
- Also: Springer Nature (User Modeling and User-Adapted Interaction journal): A survey on popularity bias in recommender systems - User Modeling and User-Adapted Interaction
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