Developing Latent Factor Recommender Systems for Implicit Feedback: Optimizing Top-k under Data Sparsity

Additional Metadata
Keywords	Recommender systems, Implicit feedback, Data sparsity, Matrix factorization, Factorization machines, Deep learning, Collaborative filtering
Thesis Advisor	De Caigny, Arno, Lemmens, Aurélie
Persistent URL	hdl.handle.net/2105/77348
Series	Business Analytics & Management
Organisation	Rotterdam School of Management
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Zamora, Jan. (2025). Developing Latent Factor Recommender Systems for Implicit Feedback: Optimizing Top-k under Data Sparsity. In Business Analytics & Management.http://hdl.handle.net/2105/77348

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