Refined co-SVD recommender algorithm: data processing and performance metrics

Low Jia Ming, Ian, Kim Teck Tan, Chern Hong Lim

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


A resurgence of research interest in recommender systems can be attributed to the widely publicized Netflix competition with the grand prize of USD 1 million. The competition enabled the promising collaborative filtering algorithms to come to prominence due to the availability of a large dataset and from it, the growth in the use of matrix factorization. There have been many recommender system projects centered around use of matrix factorization, with the co-SVD approach being one of the most promising. However, the field is
chaotic using different benchmarks and evaluation metrics. Not only the performance metrics reported are not consistent, but it is difficult to reproduce existing research when details of the data processing and hyperparameters lack clarity. This paper is to address these shortcomings and provide researchers in this field with a current baseline through the provision of detailed implementation of the co-SVD approach. To facilitate progress for future researchers, it will also provide results from an up-to-date dataset using pertinent evaluation metrics such as the top-N recommendations and the normalized discounted cumulative gain measures.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De Marsico, Gabriella Sanniti di Baja, Ana Fred
Place of PublicationSetúbal Portugal
Number of pages7
ISBN (Electronic)9789897585494
Publication statusPublished - 2022
EventInternational Conference on Pattern Recognition Applications and Methods 2011 - Lisbon, Portugal
Duration: 3 Feb 20225 Feb 2022
Conference number: 11th (Website) (Proceedings)


ConferenceInternational Conference on Pattern Recognition Applications and Methods 2011
Internet address


  • Recommender System
  • Reproducibility
  • Matrix Co-factorization
  • Top-N Recommendation

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