Efficient rectangular maximal-volume algorithm for rating elicitation in collaborative filtering

Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, Ivan Oseledets

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

    2 Citations (Scopus)

    Abstract

    Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation -A seed set of a particular size requires a rating matrix factorization of fixed rank that should coincide with that size. This is not necessarily optimal in the general case. In the current paper, we introduce a fast algorithm for an analytical generalization of this approach that we call Rectangular Maxvol. It allows the rank of factorization to be lower than the required size of the seed set. Moreover, the paper includes the theoretical analysis of the method's error, the complexity analysis of the existing methods and the comparison to the state-of-The-Art approaches.

    Original languageEnglish
    Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
    EditorsFrancesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages141-150
    Number of pages10
    ISBN (Electronic)9781509054725
    DOIs
    Publication statusPublished - 31 Jan 2017
    Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
    Duration: 12 Dec 201615 Dec 2016

    Publication series

    NameProceedings - IEEE International Conference on Data Mining, ICDM
    ISSN (Print)1550-4786

    Conference

    Conference16th IEEE International Conference on Data Mining, ICDM 2016
    Country/TerritorySpain
    CityBarcelona, Catalonia
    Period12/12/1615/12/16

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