A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Bidyut Kr. Patra, Raimo Launonen, Ville Ollikainen, Sukumar Nandi

    Research output: Contribution to journalArticleScientificpeer-review

    97 Citations (Scopus)

    Abstract

    Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations. Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain. Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated. Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users. Therefore, these measures are not suitable in a sparse data. In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users. Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity. To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF. Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics.
    Original languageEnglish
    Pages (from-to)163-177
    JournalKnowledge-Based Systems
    Volume82
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Collaborative filtering
    Recommender systems
    Similarity measure
    Coefficients

    Keywords

    • collaborative filtering
    • neighborhood based CF
    • similarity measure
    • Bhattacharyya coefficient
    • sparsity problem

    Cite this

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    title = "A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data",
    abstract = "Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations. Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain. Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated. Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users. Therefore, these measures are not suitable in a sparse data. In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users. Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity. To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF. Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics.",
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    author = "Patra, {Bidyut Kr.} and Raimo Launonen and Ville Ollikainen and Sukumar Nandi",
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    language = "English",
    volume = "82",
    pages = "163--177",
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    A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. / Patra, Bidyut Kr.; Launonen, Raimo; Ollikainen, Ville; Nandi, Sukumar.

    In: Knowledge-Based Systems, Vol. 82, 2015, p. 163-177.

    Research output: Contribution to journalArticleScientificpeer-review

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    AU - Patra, Bidyut Kr.

    AU - Launonen, Raimo

    AU - Ollikainen, Ville

    AU - Nandi, Sukumar

    PY - 2015

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    N2 - Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations. Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain. Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated. Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users. Therefore, these measures are not suitable in a sparse data. In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users. Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity. To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF. Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics.

    AB - Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations. Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain. Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated. Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users. Therefore, these measures are not suitable in a sparse data. In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users. Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity. To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF. Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics.

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    KW - neighborhood based CF

    KW - similarity measure

    KW - Bhattacharyya coefficient

    KW - sparsity problem

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