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

86 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

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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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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

TY - JOUR

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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 - similarity measure

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KW - sparsity problem

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