Clustering enhancement for a token-based recommender

    Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

    Abstract

    This paper introduces a clustering enhancement to an established token-based collaborative recommendation method (“upcv”). The method creates privacy-protecting abstractions for users and items by exchanging and collecting randomly generated N-bit values, “tokens”, in user-item transactions. The novel enhancement considers users’ random value spaces as hyperspaces in which the tokens are N-dimensionally clustered. Instead of selecting exchanged tokens at random, as in the baseline upcv, tokens are now selected from a cluster, which has the best match with item’s token collection. Recommendation quality is evaluated with the same 3.5% density data set as in a previous publication. The quantitative analysis indicates overall improvement in recommendation quality while learning time decreased without exception, up to one-third. There was improvement even when the number of exchanged tokes was exactly one, instead of over 100 in the baseline upcv. The performance improvement may be explained by the clustering enhancement inherently recognizing versatility of each individuals’ interests. The paper also presents a study with news data set, where the improvement was in coverage.

    Original languageEnglish
    JournalCEUR Workshop Proceedings
    Volume2482
    Publication statusPublished - 2019
    MoE publication typeA4 Article in a conference publication
    Event2018 Conference on Information and Knowledge Management Workshops, CIKM 2018 - Torino, Italy
    Duration: 22 Oct 2018 → …

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    title = "Clustering enhancement for a token-based recommender",
    abstract = "This paper introduces a clustering enhancement to an established token-based collaborative recommendation method (“upcv”). The method creates privacy-protecting abstractions for users and items by exchanging and collecting randomly generated N-bit values, “tokens”, in user-item transactions. The novel enhancement considers users’ random value spaces as hyperspaces in which the tokens are N-dimensionally clustered. Instead of selecting exchanged tokens at random, as in the baseline upcv, tokens are now selected from a cluster, which has the best match with item’s token collection. Recommendation quality is evaluated with the same 3.5{\%} density data set as in a previous publication. The quantitative analysis indicates overall improvement in recommendation quality while learning time decreased without exception, up to one-third. There was improvement even when the number of exchanged tokes was exactly one, instead of over 100 in the baseline upcv. The performance improvement may be explained by the clustering enhancement inherently recognizing versatility of each individuals’ interests. The paper also presents a study with news data set, where the improvement was in coverage.",
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    Clustering enhancement for a token-based recommender. / Ollikainen, Ville.

    In: CEUR Workshop Proceedings, Vol. 2482, 2019.

    Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

    TY - JOUR

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    AB - This paper introduces a clustering enhancement to an established token-based collaborative recommendation method (“upcv”). The method creates privacy-protecting abstractions for users and items by exchanging and collecting randomly generated N-bit values, “tokens”, in user-item transactions. The novel enhancement considers users’ random value spaces as hyperspaces in which the tokens are N-dimensionally clustered. Instead of selecting exchanged tokens at random, as in the baseline upcv, tokens are now selected from a cluster, which has the best match with item’s token collection. Recommendation quality is evaluated with the same 3.5% density data set as in a previous publication. The quantitative analysis indicates overall improvement in recommendation quality while learning time decreased without exception, up to one-third. There was improvement even when the number of exchanged tokes was exactly one, instead of over 100 in the baseline upcv. The performance improvement may be explained by the clustering enhancement inherently recognizing versatility of each individuals’ interests. The paper also presents a study with news data set, where the improvement was in coverage.

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    M3 - Article in a proceedings journal

    AN - SCOPUS:85075637882

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    JO - CEUR Workshop Proceedings

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