Constructing compact and effective graphs for recommender systems via node and edge aggregations

Lee, Sangkeun; Kahng, Minsuk; Lee, Sang-goo

doi:10.1016/j.eswa.2014.11.062

Title: Constructing compact and effective graphs for recommender systems via node and edge aggregations

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Abstract

Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we propose node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.

Authors:

Lee, Sangkeun ^[1]; Kahng, Minsuk ^[2]; Lee, Sang-goo ^[3]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Georgia Institute of Technology, Atlanta, GA (United States)
Seoul National Univ. (Republic of Korea)

Publication Date:: Wed Dec 10 00:00:00 EST 2014

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:: 1185808

Alternate Identifier(s):: OSTI ID: 1437067

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Journal Article: Accepted Manuscript

Journal Name:: Expert Systems with Applications

Additional Journal Information:: Journal Volume: 42; Journal Issue: 7; Journal ID: ISSN 0957-4174

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; graph; heterogeneity; recommendation; aggregation; random-walk; ranking

Citation Formats


                    Lee, Sangkeun, Kahng, Minsuk, and Lee, Sang-goo. Constructing compact and effective graphs for recommender systems via node and edge aggregations.  United States: N. p., 2014. 
        Web.  doi:10.1016/j.eswa.2014.11.062.

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                    Lee, Sangkeun, Kahng, Minsuk, & Lee, Sang-goo. Constructing compact and effective graphs for recommender systems via node and edge aggregations.  United States.  https://doi.org/10.1016/j.eswa.2014.11.062

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                    Lee, Sangkeun, Kahng, Minsuk, and Lee, Sang-goo. 2014.  
        "Constructing compact and effective graphs for recommender systems via node and edge aggregations".  United States.  https://doi.org/10.1016/j.eswa.2014.11.062.  https://www.osti.gov/servlets/purl/1185808.

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@article{osti_1185808,

  title        = {Constructing compact and effective graphs for recommender systems via node and edge aggregations},

  author       = {Lee, Sangkeun and Kahng, Minsuk and Lee, Sang-goo},

  abstractNote = {Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we propose node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.},

  doi          = {10.1016/j.eswa.2014.11.062},

  url          = {https://www.osti.gov/biblio/1185808},
  journal      = {Expert Systems with Applications},

  issn         = {0957-4174},

  number       = 7,

  volume       = 42,

  place        = {United States},

  year         = {Wed Dec 10 00:00:00 EST 2014},

  month        = {Wed Dec 10 00:00:00 EST 2014}

}

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