Grocery shopping recommendations based on basket-sensitive random walk

Ming Li, Benjamin Dias, Ian Jarman, Wael El-Deredy, Paulo J G Lisboa

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

    Abstract

    We describe a recommender system in the domain of grocery shopping. While recommender systems have been widely studied, this is mostly in relation to leisure products (e.g. movies, books and music) with non-repeated purchases. In grocery shopping, however, consumers will make multiple purchases of the same or very similar products more frequently than buying entirely new items. The proposed recommendation scheme offers several advantages in addressing the grocery shopping problem, namely: 1) a product similarity measure that suits a domain where no rating information is available; 2) a basket sensitive random walk model to approximate product similarities by exploiting incomplete neighborhood information; 3) online adaptation of the recommendation based on the current basket and 4) a new performance measure focusing on products that customers have not purchased before or purchase infrequently. Empirical results benchmarking on three real-world data sets demonstrate a performance improvement of the proposed method over other existing collaborative filtering models. Copyright 2009 ACM.
    Original languageEnglish
    Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.
    Place of PublicationNEW YORK
    PublisherAssociation for Computing Machinery
    Pages1215-1223
    Number of pages8
    ISBN (Print)9781605584959
    DOIs
    Publication statusPublished - 2009
    Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris
    Duration: 1 Jul 2009 → …

    Conference

    Conference15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
    CityParis
    Period1/07/09 → …

    Keywords

    • Algorithms
    • Experimentation
    • Performance

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