Integrating geographical and temporal influences into location recommendation: a method based on check-ins View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11-19

AUTHORS

Rui Duan, Cuiqing Jiang, Hemant K. Jain, Yong Ding, Deyou Shu

ABSTRACT

In the online-to-offline (O2O) business model, location recommendation plays an important role and is an essential component of the location-based services. The check-in data, which contains both the geographical and temporal information, has been treated as an important data source for location recommendation. Location-based collaborative filtering is a popular technique for computing location similarities to arrive at the recommendation. In this research we analyze the geographical and temporal characteristics of the user’s check-in activity and incorporate it for deriving recommendations using location-based collaborative filtering. To model the geographical proximity between the recommended location and the visited location, we first get the user’s active regions using the multiple-center discovering algorithm; we then derive the probability of visiting the unvisited locations by using the power-law distribution on the distance. The geographical proximity is derived by multiplying the visiting probability and the check-in ratio of the active region. To consider temporal information, we propose the concept of time-aware location similarity, which splits the user check-ins into twenty-four different time slots in a day. To address the sparsity problem created by splitting check-in data, we propose a mechanism to measure the similarities between time slots and use these similarities to infer the empty ratings. The geographical proximity and time-aware location similarity are integrated to generate the location similarity. We perform the experiments to verify the effectiveness of the proposed algorithm. The experimental results show the superiority of our method compared with the benchmarks. More... »

PAGES

1-18

References to SciGraph publications

  • 2007. Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices in UBIQUITOUS INTELLIGENCE AND COMPUTING
  • 2009. A Survey of Collaborative Filtering Techniques in ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2002-10. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms in INFORMATION RETRIEVAL JOURNAL
  • 2014. Location-Based Social Networks in RECOMMENDER SYSTEMS FOR LOCATION-BASED SOCIAL NETWORKS
  • 2015-07. Recommendations in location-based social networks: a survey in GEOINFORMATICA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10799-018-0293-4

    DOI

    http://dx.doi.org/10.1007/s10799-018-0293-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110032777


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information Systems", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Hefei University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.256896.6", 
              "name": [
                "National Engineering Lab for Public Security Risk Perception and Control by Big Data (PSRPC), China, Academy of Electronics and Information Technology, 100041, Beijing, People\u2019s Republic of China", 
                "School of Management, Hefei University of Technology, No. 193, Tunxi Road, 230009, Hefei, Anhui, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Duan", 
            "givenName": "Rui", 
            "id": "sg:person.013721626335.10", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013721626335.10"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Hefei University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.256896.6", 
              "name": [
                "School of Management, Hefei University of Technology, No. 193, Tunxi Road, 230009, Hefei, Anhui, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jiang", 
            "givenName": "Cuiqing", 
            "id": "sg:person.010503555327.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010503555327.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Tennessee at Chattanooga", 
              "id": "https://www.grid.ac/institutes/grid.267303.3", 
              "name": [
                "College of Business, University of Tennessee\u2013Chattanooga, 37403, Tennessee, TN, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jain", 
            "givenName": "Hemant K.", 
            "id": "sg:person.014404547615.10", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014404547615.10"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Hefei University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.256896.6", 
              "name": [
                "School of Management, Hefei University of Technology, No. 193, Tunxi Road, 230009, Hefei, Anhui, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ding", 
            "givenName": "Yong", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Hefei University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.256896.6", 
              "name": [
                "School of Management, Hefei University of Technology, No. 193, Tunxi Road, 230009, Hefei, Anhui, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shu", 
            "givenName": "Deyou", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/2484028.2484030", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001797823"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10707-014-0220-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003273098", 
              "https://doi.org/10.1007/s10707-014-0220-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.dss.2015.05.013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005036779"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.dss.2013.09.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010975306"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1869790.1869861", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011145608"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2507157.2507182", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012884906"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2505515.2505637", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013836577"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2507157.2507174", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017904492"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2030112.2030140", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024503072"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.dss.2012.05.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024830946"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1020443909834", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026775597", 
              "https://doi.org/10.1023/a:1020443909834"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2433396.2433444", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029493279"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4939-0286-6_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029828097", 
              "https://doi.org/10.1007/978-1-4939-0286-6_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2505821.2505823", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030247643"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-73549-6_110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031734264", 
              "https://doi.org/10.1007/978-3-540-73549-6_110"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-73549-6_110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031734264", 
              "https://doi.org/10.1007/978-3-540-73549-6_110"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2009916.2009960", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034456769"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1155/2009/421425", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036297015", 
              "https://doi.org/10.1155/2009/421425"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2525314.2525357", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036979165"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2505515.2505616", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038264539"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2464464.2464479", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042369894"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1526709.1526816", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043251722"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2339530.2339574", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045749757"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.chb.2014.12.038", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045830594"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2525314.2525339", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046090023"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2009916.2009962", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049483033"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2005.99", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061661493"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9781611972832.19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1088800703"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2018-11-19", 
        "datePublishedReg": "2018-11-19", 
        "description": "In the online-to-offline (O2O) business model, location recommendation plays an important role and is an essential component of the location-based services. The check-in data, which contains both the geographical and temporal information, has been treated as an important data source for location recommendation. Location-based collaborative filtering is a popular technique for computing location similarities to arrive at the recommendation. In this research we analyze the geographical and temporal characteristics of the user\u2019s check-in activity and incorporate it for deriving recommendations using location-based collaborative filtering. To model the geographical proximity between the recommended location and the visited location, we first get the user\u2019s active regions using the multiple-center discovering algorithm; we then derive the probability of visiting the unvisited locations by using the power-law distribution on the distance. The geographical proximity is derived by multiplying the visiting probability and the check-in ratio of the active region. To consider temporal information, we propose the concept of time-aware location similarity, which splits the user check-ins into twenty-four different time slots in a day. To address the sparsity problem created by splitting check-in data, we propose a mechanism to measure the similarities between time slots and use these similarities to infer the empty ratings. The geographical proximity and time-aware location similarity are integrated to generate the location similarity. We perform the experiments to verify the effectiveness of the proposed algorithm. The experimental results show the superiority of our method compared with the benchmarks.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10799-018-0293-4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1136467", 
            "issn": [
              "1385-951X", 
              "1573-7667"
            ], 
            "name": "Information Technology and Management", 
            "type": "Periodical"
          }
        ], 
        "name": "Integrating geographical and temporal influences into location recommendation: a method based on check-ins", 
        "pagination": "1-18", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "7c1e405e27a0d83517396253ce59d973b1241e7729a5bbc32a1b14d82e58b61b"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10799-018-0293-4"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1110032777"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10799-018-0293-4", 
          "https://app.dimensions.ai/details/publication/pub.1110032777"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T08:09", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000267_0000000267/records_56107_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs10799-018-0293-4"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10799-018-0293-4'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10799-018-0293-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10799-018-0293-4'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10799-018-0293-4'


     

    This table displays all metadata directly associated to this object as RDF triples.

    171 TRIPLES      21 PREDICATES      51 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10799-018-0293-4 schema:about anzsrc-for:08
    2 anzsrc-for:0806
    3 schema:author N21abb3e6c1db4e829cba70de916a5302
    4 schema:citation sg:pub.10.1007/978-1-4939-0286-6_4
    5 sg:pub.10.1007/978-3-540-73549-6_110
    6 sg:pub.10.1007/s10707-014-0220-8
    7 sg:pub.10.1023/a:1020443909834
    8 sg:pub.10.1155/2009/421425
    9 https://doi.org/10.1016/j.chb.2014.12.038
    10 https://doi.org/10.1016/j.dss.2012.05.012
    11 https://doi.org/10.1016/j.dss.2013.09.010
    12 https://doi.org/10.1016/j.dss.2015.05.013
    13 https://doi.org/10.1109/tkde.2005.99
    14 https://doi.org/10.1137/1.9781611972832.19
    15 https://doi.org/10.1145/1526709.1526816
    16 https://doi.org/10.1145/1869790.1869861
    17 https://doi.org/10.1145/2009916.2009960
    18 https://doi.org/10.1145/2009916.2009962
    19 https://doi.org/10.1145/2030112.2030140
    20 https://doi.org/10.1145/2339530.2339574
    21 https://doi.org/10.1145/2433396.2433444
    22 https://doi.org/10.1145/2464464.2464479
    23 https://doi.org/10.1145/2484028.2484030
    24 https://doi.org/10.1145/2505515.2505616
    25 https://doi.org/10.1145/2505515.2505637
    26 https://doi.org/10.1145/2505821.2505823
    27 https://doi.org/10.1145/2507157.2507174
    28 https://doi.org/10.1145/2507157.2507182
    29 https://doi.org/10.1145/2525314.2525339
    30 https://doi.org/10.1145/2525314.2525357
    31 schema:datePublished 2018-11-19
    32 schema:datePublishedReg 2018-11-19
    33 schema:description In the online-to-offline (O2O) business model, location recommendation plays an important role and is an essential component of the location-based services. The check-in data, which contains both the geographical and temporal information, has been treated as an important data source for location recommendation. Location-based collaborative filtering is a popular technique for computing location similarities to arrive at the recommendation. In this research we analyze the geographical and temporal characteristics of the user’s check-in activity and incorporate it for deriving recommendations using location-based collaborative filtering. To model the geographical proximity between the recommended location and the visited location, we first get the user’s active regions using the multiple-center discovering algorithm; we then derive the probability of visiting the unvisited locations by using the power-law distribution on the distance. The geographical proximity is derived by multiplying the visiting probability and the check-in ratio of the active region. To consider temporal information, we propose the concept of time-aware location similarity, which splits the user check-ins into twenty-four different time slots in a day. To address the sparsity problem created by splitting check-in data, we propose a mechanism to measure the similarities between time slots and use these similarities to infer the empty ratings. The geographical proximity and time-aware location similarity are integrated to generate the location similarity. We perform the experiments to verify the effectiveness of the proposed algorithm. The experimental results show the superiority of our method compared with the benchmarks.
    34 schema:genre research_article
    35 schema:inLanguage en
    36 schema:isAccessibleForFree false
    37 schema:isPartOf sg:journal.1136467
    38 schema:name Integrating geographical and temporal influences into location recommendation: a method based on check-ins
    39 schema:pagination 1-18
    40 schema:productId N1d181ed64f6f491c9578e641d6d95ecd
    41 Nd2e56ff2a17b4c30a5f6b1e0dc0d99ba
    42 Nf92cc938657b4604b91353a838fdf433
    43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110032777
    44 https://doi.org/10.1007/s10799-018-0293-4
    45 schema:sdDatePublished 2019-04-11T08:09
    46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    47 schema:sdPublisher Nf96ae47d9b9f42279ee6f96a767b350c
    48 schema:url https://link.springer.com/10.1007%2Fs10799-018-0293-4
    49 sgo:license sg:explorer/license/
    50 sgo:sdDataset articles
    51 rdf:type schema:ScholarlyArticle
    52 N1d181ed64f6f491c9578e641d6d95ecd schema:name doi
    53 schema:value 10.1007/s10799-018-0293-4
    54 rdf:type schema:PropertyValue
    55 N21abb3e6c1db4e829cba70de916a5302 rdf:first sg:person.013721626335.10
    56 rdf:rest Ne2df0b9fd9904a1a8519af17df734429
    57 N2a6e630b902847b68f1a894b3be496fb rdf:first sg:person.014404547615.10
    58 rdf:rest Nd38d53fba9784f7a8832d828ff920af7
    59 N44ff69a02c274ea4b39d065124bb6f08 schema:affiliation https://www.grid.ac/institutes/grid.256896.6
    60 schema:familyName Shu
    61 schema:givenName Deyou
    62 rdf:type schema:Person
    63 N97587cdd4aca43f59dbaea5c308c18ac schema:affiliation https://www.grid.ac/institutes/grid.256896.6
    64 schema:familyName Ding
    65 schema:givenName Yong
    66 rdf:type schema:Person
    67 Nd2e56ff2a17b4c30a5f6b1e0dc0d99ba schema:name readcube_id
    68 schema:value 7c1e405e27a0d83517396253ce59d973b1241e7729a5bbc32a1b14d82e58b61b
    69 rdf:type schema:PropertyValue
    70 Nd38d53fba9784f7a8832d828ff920af7 rdf:first N97587cdd4aca43f59dbaea5c308c18ac
    71 rdf:rest Nefa5f2a8ee604209871c0ac69aaeffcd
    72 Ne2df0b9fd9904a1a8519af17df734429 rdf:first sg:person.010503555327.26
    73 rdf:rest N2a6e630b902847b68f1a894b3be496fb
    74 Nefa5f2a8ee604209871c0ac69aaeffcd rdf:first N44ff69a02c274ea4b39d065124bb6f08
    75 rdf:rest rdf:nil
    76 Nf92cc938657b4604b91353a838fdf433 schema:name dimensions_id
    77 schema:value pub.1110032777
    78 rdf:type schema:PropertyValue
    79 Nf96ae47d9b9f42279ee6f96a767b350c schema:name Springer Nature - SN SciGraph project
    80 rdf:type schema:Organization
    81 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    82 schema:name Information and Computing Sciences
    83 rdf:type schema:DefinedTerm
    84 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    85 schema:name Information Systems
    86 rdf:type schema:DefinedTerm
    87 sg:journal.1136467 schema:issn 1385-951X
    88 1573-7667
    89 schema:name Information Technology and Management
    90 rdf:type schema:Periodical
    91 sg:person.010503555327.26 schema:affiliation https://www.grid.ac/institutes/grid.256896.6
    92 schema:familyName Jiang
    93 schema:givenName Cuiqing
    94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010503555327.26
    95 rdf:type schema:Person
    96 sg:person.013721626335.10 schema:affiliation https://www.grid.ac/institutes/grid.256896.6
    97 schema:familyName Duan
    98 schema:givenName Rui
    99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013721626335.10
    100 rdf:type schema:Person
    101 sg:person.014404547615.10 schema:affiliation https://www.grid.ac/institutes/grid.267303.3
    102 schema:familyName Jain
    103 schema:givenName Hemant K.
    104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014404547615.10
    105 rdf:type schema:Person
    106 sg:pub.10.1007/978-1-4939-0286-6_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029828097
    107 https://doi.org/10.1007/978-1-4939-0286-6_4
    108 rdf:type schema:CreativeWork
    109 sg:pub.10.1007/978-3-540-73549-6_110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031734264
    110 https://doi.org/10.1007/978-3-540-73549-6_110
    111 rdf:type schema:CreativeWork
    112 sg:pub.10.1007/s10707-014-0220-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003273098
    113 https://doi.org/10.1007/s10707-014-0220-8
    114 rdf:type schema:CreativeWork
    115 sg:pub.10.1023/a:1020443909834 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026775597
    116 https://doi.org/10.1023/a:1020443909834
    117 rdf:type schema:CreativeWork
    118 sg:pub.10.1155/2009/421425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036297015
    119 https://doi.org/10.1155/2009/421425
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1016/j.chb.2014.12.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045830594
    122 rdf:type schema:CreativeWork
    123 https://doi.org/10.1016/j.dss.2012.05.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024830946
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1016/j.dss.2013.09.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010975306
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1016/j.dss.2015.05.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005036779
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1109/tkde.2005.99 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061661493
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1137/1.9781611972832.19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088800703
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1145/1526709.1526816 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043251722
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1145/1869790.1869861 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011145608
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1145/2009916.2009960 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034456769
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1145/2009916.2009962 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049483033
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1145/2030112.2030140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024503072
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1145/2339530.2339574 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045749757
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1145/2433396.2433444 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029493279
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1145/2464464.2464479 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042369894
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1145/2484028.2484030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001797823
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1145/2505515.2505616 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038264539
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1145/2505515.2505637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013836577
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1145/2505821.2505823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030247643
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1145/2507157.2507174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017904492
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1145/2507157.2507182 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012884906
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1145/2525314.2525339 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046090023
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1145/2525314.2525357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036979165
    164 rdf:type schema:CreativeWork
    165 https://www.grid.ac/institutes/grid.256896.6 schema:alternateName Hefei University of Technology
    166 schema:name National Engineering Lab for Public Security Risk Perception and Control by Big Data (PSRPC), China, Academy of Electronics and Information Technology, 100041, Beijing, People’s Republic of China
    167 School of Management, Hefei University of Technology, No. 193, Tunxi Road, 230009, Hefei, Anhui, People’s Republic of China
    168 rdf:type schema:Organization
    169 https://www.grid.ac/institutes/grid.267303.3 schema:alternateName University of Tennessee at Chattanooga
    170 schema:name College of Business, University of Tennessee–Chattanooga, 37403, Tennessee, TN, USA
    171 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...