User interest prediction over future unobserved topics on social networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Fattane Zarrinkalam, Mohsen Kahani, Ebrahim Bagheri

ABSTRACT

The accurate prediction of users’ future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current interests and little work has been done on the prediction of user potential interests in the future. There have been work that attempt to identify a user future interests, however they cannot predict user interests with regard to new topics since these topics have never received any feedback from users in the past. In this paper, we propose a framework that works on the basis of temporal evolution of user interests and utilizes semantic information from knowledge bases such as Wikipedia to predict user future interests and overcome the cold item problem. Through extensive experiments on a real-world Twitter dataset, we demonstrate the effectiveness of our approach in predicting future interests of users compared to state-of-the-art baselines. Moreover, we further show that the impact of our work is especially meaningful when considered in case of cold items. More... »

PAGES

93-128

References to SciGraph publications

  • 2011. Analyzing User Modeling on Twitter for Personalized News Recommendations in USER MODELING, ADAPTION AND PERSONALIZATION
  • 2014. User Interests Identification on Twitter Using a Hierarchical Knowledge Base in THE SEMANTIC WEB: TRENDS AND CHALLENGES
  • 2011. Influence and Passivity in Social Media in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2010. dbrec — Music Recommendations Using DBpedia in THE SEMANTIC WEB – ISWC 2010
  • 2016. Inferring Implicit Topical Interests on Twitter in ADVANCES IN INFORMATION RETRIEVAL
  • 2012. Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy in THE SEMANTIC WEB – ISWC 2012
  • 2017. Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies in ADVANCES IN INFORMATION RETRIEVAL
  • 2017. Predicting Users’ Future Interests on Twitter in ADVANCES IN INFORMATION RETRIEVAL
  • 2011. Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web in THE SEMANIC WEB: RESEARCH AND APPLICATIONS
  • 2011. Comparing Twitter and Traditional Media Using Topic Models in ADVANCES IN INFORMATION RETRIEVAL
  • 2017-04. Attributes coupling based matrix factorization for item recommendation in APPLIED INTELLIGENCE
  • 2015. Temporal Latent Topic User Profiles for Search Personalisation in ADVANCES IN INFORMATION RETRIEVAL
  • 2017-03. A probabilistic method for emerging topic tracking in Microblog stream in WORLD WIDE WEB
  • 2016-05-13. Time-Sensitive Topic-Based Communities on Twitter in ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2015. Content-Based Recommendations via DBpedia and Freebase: A Case Study in the Music Domain in THE SEMANTIC WEB - ISWC 2015
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10791-018-9337-y

    DOI

    http://dx.doi.org/10.1007/s10791-018-9337-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1105467215


    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": "Ryerson University", 
              "id": "https://www.grid.ac/institutes/grid.68312.3e", 
              "name": [
                "Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran", 
                "Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Toronto, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zarrinkalam", 
            "givenName": "Fattane", 
            "id": "sg:person.07427631405.20", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07427631405.20"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Ferdowsi University of Mashhad", 
              "id": "https://www.grid.ac/institutes/grid.411301.6", 
              "name": [
                "Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kahani", 
            "givenName": "Mohsen", 
            "id": "sg:person.012546312555.90", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012546312555.90"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Ryerson University", 
              "id": "https://www.grid.ac/institutes/grid.68312.3e", 
              "name": [
                "Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Toronto, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bagheri", 
            "givenName": "Ebrahim", 
            "id": "sg:person.01354270666.43", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354270666.43"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-17749-1_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000769943", 
              "https://doi.org/10.1007/978-3-642-17749-1_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-17749-1_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000769943", 
              "https://doi.org/10.1007/978-3-642-17749-1_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2806416.2806470", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000971078"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-22362-4_1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001407363", 
              "https://doi.org/10.1007/978-3-642-22362-4_1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-22362-4_1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001407363", 
              "https://doi.org/10.1007/978-3-642-22362-4_1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-23808-6_2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002866566", 
              "https://doi.org/10.1007/978-3-642-23808-6_2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-23808-6_2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002866566", 
              "https://doi.org/10.1007/978-3-642-23808-6_2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-34111-8_25", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006740633", 
              "https://doi.org/10.1007/978-3-319-34111-8_25"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-34111-8_25", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006740633", 
              "https://doi.org/10.1007/978-3-319-34111-8_25"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2187836.2187872", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007962764"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-21064-8_26", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008619230", 
              "https://doi.org/10.1007/978-3-642-21064-8_26"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.dss.2013.02.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009781668"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2911451.2914726", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011315187"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2939672.2939673", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011682859"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2133806.2133826", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012425283"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-20161-5_34", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012531775", 
              "https://doi.org/10.1007/978-3-642-20161-5_34"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-20161-5_34", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012531775", 
              "https://doi.org/10.1007/978-3-642-20161-5_34"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1871840.1871852", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012892827"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-16354-3_67", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013604009", 
              "https://doi.org/10.1007/978-3-319-16354-3_67"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2484028.2484166", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014502103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2872518.2891111", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015176934"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2362499.2362501", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017746506"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2527031.2527040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019692775"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-35176-1_23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022125851", 
              "https://doi.org/10.1007/978-3-642-35176-1_23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2013.03.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025111269"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-07443-6_8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027091017", 
              "https://doi.org/10.1007/978-3-319-07443-6_8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-25007-6_35", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028550193", 
              "https://doi.org/10.1007/978-3-319-25007-6_35"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2993318.2993332", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028692251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1835804.1835896", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031260261"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eswa.2013.11.020", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031872742"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2488388.2488411", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034672697"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1718487.1718520", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037087000"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.websem.2014.04.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041017096"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1835449.1835643", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044013281"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-30671-1_35", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044411514", 
              "https://doi.org/10.1007/978-3-319-30671-1_35"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2699670", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047718486"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2507157.2507172", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048602396"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10489-016-0841-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050152688", 
              "https://doi.org/10.1007/s10489-016-0841-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10489-016-0841-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050152688", 
              "https://doi.org/10.1007/s10489-016-0841-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1721654.1721677", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050843603"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1753326.1753503", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051119691"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11280-016-0390-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053075668", 
              "https://doi.org/10.1007/s11280-016-0390-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ms.2011.122", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061421272"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2014.2313872", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061662879"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1561/1100000009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068001170"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.5539/cis.v6n4p59", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1072944280"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-56608-5_36", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084757398", 
              "https://doi.org/10.1007/978-3-319-56608-5_36"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-56608-5_10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084761753", 
              "https://doi.org/10.1007/978-3-319-56608-5_10"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2017.2722411", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090555496"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/3072606", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090745082"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.websem.2017.05.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091494816"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/wi-iat.2010.63", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093851071"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/wi-iat.2015.182", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094690661"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/wi-iat.2011.47", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094931840"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ipm.2017.12.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099744822"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04", 
        "datePublishedReg": "2019-04-01", 
        "description": "The accurate prediction of users\u2019 future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current interests and little work has been done on the prediction of user potential interests in the future. There have been work that attempt to identify a user future interests, however they cannot predict user interests with regard to new topics since these topics have never received any feedback from users in the past. In this paper, we propose a framework that works on the basis of temporal evolution of user interests and utilizes semantic information from knowledge bases such as Wikipedia to predict user future interests and overcome the cold item problem. Through extensive experiments on a real-world Twitter dataset, we demonstrate the effectiveness of our approach in predicting future interests of users compared to state-of-the-art baselines. Moreover, we further show that the impact of our work is especially meaningful when considered in case of cold items.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10791-018-9337-y", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1023664", 
            "issn": [
              "1386-4564", 
              "1573-7659"
            ], 
            "name": "Information Retrieval Journal", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1-2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "22"
          }
        ], 
        "name": "User interest prediction over future unobserved topics on social networks", 
        "pagination": "93-128", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "eecf1cb7d887ced417918abfe75c447249f1e65f8393b01b0a89c49b62853cde"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10791-018-9337-y"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1105467215"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10791-018-9337-y", 
          "https://app.dimensions.ai/details/publication/pub.1105467215"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T11:05", 
        "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/v90/0000000352_0000000352/records_60369_00000003.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs10791-018-9337-y"
      }
    ]
     

    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/s10791-018-9337-y'

    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/s10791-018-9337-y'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10791-018-9337-y'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10791-018-9337-y'


     

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

    241 TRIPLES      21 PREDICATES      76 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10791-018-9337-y schema:about anzsrc-for:08
    2 anzsrc-for:0806
    3 schema:author N4ac5786c177c4870a8c458bb1704e970
    4 schema:citation sg:pub.10.1007/978-3-319-07443-6_8
    5 sg:pub.10.1007/978-3-319-16354-3_67
    6 sg:pub.10.1007/978-3-319-25007-6_35
    7 sg:pub.10.1007/978-3-319-30671-1_35
    8 sg:pub.10.1007/978-3-319-34111-8_25
    9 sg:pub.10.1007/978-3-319-56608-5_10
    10 sg:pub.10.1007/978-3-319-56608-5_36
    11 sg:pub.10.1007/978-3-642-17749-1_14
    12 sg:pub.10.1007/978-3-642-20161-5_34
    13 sg:pub.10.1007/978-3-642-21064-8_26
    14 sg:pub.10.1007/978-3-642-22362-4_1
    15 sg:pub.10.1007/978-3-642-23808-6_2
    16 sg:pub.10.1007/978-3-642-35176-1_23
    17 sg:pub.10.1007/s10489-016-0841-8
    18 sg:pub.10.1007/s11280-016-0390-4
    19 https://doi.org/10.1016/j.dss.2013.02.007
    20 https://doi.org/10.1016/j.eswa.2013.11.020
    21 https://doi.org/10.1016/j.ipm.2017.12.003
    22 https://doi.org/10.1016/j.knosys.2013.03.012
    23 https://doi.org/10.1016/j.websem.2014.04.001
    24 https://doi.org/10.1016/j.websem.2017.05.004
    25 https://doi.org/10.1109/ms.2011.122
    26 https://doi.org/10.1109/tkde.2014.2313872
    27 https://doi.org/10.1109/tkde.2017.2722411
    28 https://doi.org/10.1109/wi-iat.2010.63
    29 https://doi.org/10.1109/wi-iat.2011.47
    30 https://doi.org/10.1109/wi-iat.2015.182
    31 https://doi.org/10.1145/1718487.1718520
    32 https://doi.org/10.1145/1721654.1721677
    33 https://doi.org/10.1145/1753326.1753503
    34 https://doi.org/10.1145/1835449.1835643
    35 https://doi.org/10.1145/1835804.1835896
    36 https://doi.org/10.1145/1871840.1871852
    37 https://doi.org/10.1145/2133806.2133826
    38 https://doi.org/10.1145/2187836.2187872
    39 https://doi.org/10.1145/2362499.2362501
    40 https://doi.org/10.1145/2484028.2484166
    41 https://doi.org/10.1145/2488388.2488411
    42 https://doi.org/10.1145/2507157.2507172
    43 https://doi.org/10.1145/2527031.2527040
    44 https://doi.org/10.1145/2699670
    45 https://doi.org/10.1145/2806416.2806470
    46 https://doi.org/10.1145/2872518.2891111
    47 https://doi.org/10.1145/2911451.2914726
    48 https://doi.org/10.1145/2939672.2939673
    49 https://doi.org/10.1145/2993318.2993332
    50 https://doi.org/10.1145/3072606
    51 https://doi.org/10.1561/1100000009
    52 https://doi.org/10.5539/cis.v6n4p59
    53 schema:datePublished 2019-04
    54 schema:datePublishedReg 2019-04-01
    55 schema:description The accurate prediction of users’ future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current interests and little work has been done on the prediction of user potential interests in the future. There have been work that attempt to identify a user future interests, however they cannot predict user interests with regard to new topics since these topics have never received any feedback from users in the past. In this paper, we propose a framework that works on the basis of temporal evolution of user interests and utilizes semantic information from knowledge bases such as Wikipedia to predict user future interests and overcome the cold item problem. Through extensive experiments on a real-world Twitter dataset, we demonstrate the effectiveness of our approach in predicting future interests of users compared to state-of-the-art baselines. Moreover, we further show that the impact of our work is especially meaningful when considered in case of cold items.
    56 schema:genre research_article
    57 schema:inLanguage en
    58 schema:isAccessibleForFree false
    59 schema:isPartOf N9146b7546edc4478a11a081b9d88b29e
    60 Nde0fc3e859484ea0a3842c8681f11727
    61 sg:journal.1023664
    62 schema:name User interest prediction over future unobserved topics on social networks
    63 schema:pagination 93-128
    64 schema:productId N9509ac7feaaf49e6822e9775c8c48af2
    65 Nf83f03b8e23b4a5ab2204ee85ea81f16
    66 Nf8550004fe1c484392bc3a31d67a0d43
    67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105467215
    68 https://doi.org/10.1007/s10791-018-9337-y
    69 schema:sdDatePublished 2019-04-11T11:05
    70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    71 schema:sdPublisher N24310fe055a14a88abe2a30d3ddc6472
    72 schema:url https://link.springer.com/10.1007%2Fs10791-018-9337-y
    73 sgo:license sg:explorer/license/
    74 sgo:sdDataset articles
    75 rdf:type schema:ScholarlyArticle
    76 N048c3d2c6f5b4798a827d4d0fd9d8a38 rdf:first sg:person.012546312555.90
    77 rdf:rest Ne4f081ff3a184bd1ac6fa4e210a09e56
    78 N24310fe055a14a88abe2a30d3ddc6472 schema:name Springer Nature - SN SciGraph project
    79 rdf:type schema:Organization
    80 N4ac5786c177c4870a8c458bb1704e970 rdf:first sg:person.07427631405.20
    81 rdf:rest N048c3d2c6f5b4798a827d4d0fd9d8a38
    82 N9146b7546edc4478a11a081b9d88b29e schema:volumeNumber 22
    83 rdf:type schema:PublicationVolume
    84 N9509ac7feaaf49e6822e9775c8c48af2 schema:name dimensions_id
    85 schema:value pub.1105467215
    86 rdf:type schema:PropertyValue
    87 Nde0fc3e859484ea0a3842c8681f11727 schema:issueNumber 1-2
    88 rdf:type schema:PublicationIssue
    89 Ne4f081ff3a184bd1ac6fa4e210a09e56 rdf:first sg:person.01354270666.43
    90 rdf:rest rdf:nil
    91 Nf83f03b8e23b4a5ab2204ee85ea81f16 schema:name doi
    92 schema:value 10.1007/s10791-018-9337-y
    93 rdf:type schema:PropertyValue
    94 Nf8550004fe1c484392bc3a31d67a0d43 schema:name readcube_id
    95 schema:value eecf1cb7d887ced417918abfe75c447249f1e65f8393b01b0a89c49b62853cde
    96 rdf:type schema:PropertyValue
    97 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Information and Computing Sciences
    99 rdf:type schema:DefinedTerm
    100 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Information Systems
    102 rdf:type schema:DefinedTerm
    103 sg:journal.1023664 schema:issn 1386-4564
    104 1573-7659
    105 schema:name Information Retrieval Journal
    106 rdf:type schema:Periodical
    107 sg:person.012546312555.90 schema:affiliation https://www.grid.ac/institutes/grid.411301.6
    108 schema:familyName Kahani
    109 schema:givenName Mohsen
    110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012546312555.90
    111 rdf:type schema:Person
    112 sg:person.01354270666.43 schema:affiliation https://www.grid.ac/institutes/grid.68312.3e
    113 schema:familyName Bagheri
    114 schema:givenName Ebrahim
    115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354270666.43
    116 rdf:type schema:Person
    117 sg:person.07427631405.20 schema:affiliation https://www.grid.ac/institutes/grid.68312.3e
    118 schema:familyName Zarrinkalam
    119 schema:givenName Fattane
    120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07427631405.20
    121 rdf:type schema:Person
    122 sg:pub.10.1007/978-3-319-07443-6_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027091017
    123 https://doi.org/10.1007/978-3-319-07443-6_8
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/978-3-319-16354-3_67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013604009
    126 https://doi.org/10.1007/978-3-319-16354-3_67
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1007/978-3-319-25007-6_35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028550193
    129 https://doi.org/10.1007/978-3-319-25007-6_35
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1007/978-3-319-30671-1_35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044411514
    132 https://doi.org/10.1007/978-3-319-30671-1_35
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1007/978-3-319-34111-8_25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006740633
    135 https://doi.org/10.1007/978-3-319-34111-8_25
    136 rdf:type schema:CreativeWork
    137 sg:pub.10.1007/978-3-319-56608-5_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084761753
    138 https://doi.org/10.1007/978-3-319-56608-5_10
    139 rdf:type schema:CreativeWork
    140 sg:pub.10.1007/978-3-319-56608-5_36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084757398
    141 https://doi.org/10.1007/978-3-319-56608-5_36
    142 rdf:type schema:CreativeWork
    143 sg:pub.10.1007/978-3-642-17749-1_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000769943
    144 https://doi.org/10.1007/978-3-642-17749-1_14
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1007/978-3-642-20161-5_34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012531775
    147 https://doi.org/10.1007/978-3-642-20161-5_34
    148 rdf:type schema:CreativeWork
    149 sg:pub.10.1007/978-3-642-21064-8_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008619230
    150 https://doi.org/10.1007/978-3-642-21064-8_26
    151 rdf:type schema:CreativeWork
    152 sg:pub.10.1007/978-3-642-22362-4_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001407363
    153 https://doi.org/10.1007/978-3-642-22362-4_1
    154 rdf:type schema:CreativeWork
    155 sg:pub.10.1007/978-3-642-23808-6_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002866566
    156 https://doi.org/10.1007/978-3-642-23808-6_2
    157 rdf:type schema:CreativeWork
    158 sg:pub.10.1007/978-3-642-35176-1_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022125851
    159 https://doi.org/10.1007/978-3-642-35176-1_23
    160 rdf:type schema:CreativeWork
    161 sg:pub.10.1007/s10489-016-0841-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050152688
    162 https://doi.org/10.1007/s10489-016-0841-8
    163 rdf:type schema:CreativeWork
    164 sg:pub.10.1007/s11280-016-0390-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053075668
    165 https://doi.org/10.1007/s11280-016-0390-4
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1016/j.dss.2013.02.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009781668
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1016/j.eswa.2013.11.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031872742
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1016/j.ipm.2017.12.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099744822
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1016/j.knosys.2013.03.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025111269
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1016/j.websem.2014.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041017096
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1016/j.websem.2017.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091494816
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1109/ms.2011.122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061421272
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1109/tkde.2014.2313872 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662879
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1109/tkde.2017.2722411 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090555496
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.1109/wi-iat.2010.63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093851071
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.1109/wi-iat.2011.47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094931840
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1109/wi-iat.2015.182 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094690661
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1145/1718487.1718520 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037087000
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1145/1721654.1721677 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050843603
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1145/1753326.1753503 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051119691
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1145/1835449.1835643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044013281
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.1145/1835804.1835896 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031260261
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.1145/1871840.1871852 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012892827
    202 rdf:type schema:CreativeWork
    203 https://doi.org/10.1145/2133806.2133826 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012425283
    204 rdf:type schema:CreativeWork
    205 https://doi.org/10.1145/2187836.2187872 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007962764
    206 rdf:type schema:CreativeWork
    207 https://doi.org/10.1145/2362499.2362501 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017746506
    208 rdf:type schema:CreativeWork
    209 https://doi.org/10.1145/2484028.2484166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014502103
    210 rdf:type schema:CreativeWork
    211 https://doi.org/10.1145/2488388.2488411 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034672697
    212 rdf:type schema:CreativeWork
    213 https://doi.org/10.1145/2507157.2507172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048602396
    214 rdf:type schema:CreativeWork
    215 https://doi.org/10.1145/2527031.2527040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019692775
    216 rdf:type schema:CreativeWork
    217 https://doi.org/10.1145/2699670 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047718486
    218 rdf:type schema:CreativeWork
    219 https://doi.org/10.1145/2806416.2806470 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000971078
    220 rdf:type schema:CreativeWork
    221 https://doi.org/10.1145/2872518.2891111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015176934
    222 rdf:type schema:CreativeWork
    223 https://doi.org/10.1145/2911451.2914726 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011315187
    224 rdf:type schema:CreativeWork
    225 https://doi.org/10.1145/2939672.2939673 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011682859
    226 rdf:type schema:CreativeWork
    227 https://doi.org/10.1145/2993318.2993332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028692251
    228 rdf:type schema:CreativeWork
    229 https://doi.org/10.1145/3072606 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090745082
    230 rdf:type schema:CreativeWork
    231 https://doi.org/10.1561/1100000009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068001170
    232 rdf:type schema:CreativeWork
    233 https://doi.org/10.5539/cis.v6n4p59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072944280
    234 rdf:type schema:CreativeWork
    235 https://www.grid.ac/institutes/grid.411301.6 schema:alternateName Ferdowsi University of Mashhad
    236 schema:name Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
    237 rdf:type schema:Organization
    238 https://www.grid.ac/institutes/grid.68312.3e schema:alternateName Ryerson University
    239 schema:name Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
    240 Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Toronto, Canada
    241 rdf:type schema:Organization
     




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


    ...