Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-01

AUTHORS

Tomohiro Tsunoda, Masaaki Hoshino

ABSTRACT

TV Program recommendation is a good example of a novel application of networked appliances using personalization technologies. The aim of this paper is to propose methods to improve the accuracy of TV program recommendation. Automatic metadata expansion (AME) is a method to enhance TV program metadata from electronic program guide (EPG) data, and indirect collaborative filtering (ICF) is a method to recommend non-persistent items such as TV programs based on the preferences of other members in a community. In this paper, the effectiveness of these methods is confirmed through experiments. This online TV recommendation system is currently being used by 230,000 members in Japan. The result of the actual operation is also discussed. More... »

PAGES

37-54

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-006-0077-4

DOI

http://dx.doi.org/10.1007/s11042-006-0077-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023261670


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": "Sony (Japan)", 
          "id": "https://www.grid.ac/institutes/grid.410792.9", 
          "name": [
            "PAO Group, Intelligent Systems Research Laboratory, Information Technologies Laboratories, Sony Corporation, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tsunoda", 
        "givenName": "Tomohiro", 
        "id": "sg:person.012353716401.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012353716401.84"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sony (Japan)", 
          "id": "https://www.grid.ac/institutes/grid.410792.9", 
          "name": [
            "PAO Group, Intelligent Systems Research Laboratory, Information Technologies Laboratories, Sony Corporation, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hoshino", 
        "givenName": "Masaaki", 
        "id": "sg:person.013746657401.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013746657401.09"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/1-4020-2164-x_1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024294478", 
          "https://doi.org/10.1007/1-4020-2164-x_1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/500141.500238", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028525891"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/192844.192905", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051044947"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/358916.358995", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053251882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/52.582976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061185738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mic.2003.1167344", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061403307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ccnc.2005.1405145", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093579844"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008-01", 
    "datePublishedReg": "2008-01-01", 
    "description": "TV Program recommendation is a good example of a novel application of networked appliances using personalization technologies. The aim of this paper is to propose methods to improve the accuracy of TV program recommendation. Automatic metadata expansion (AME) is a method to enhance TV program metadata from electronic program guide (EPG) data, and indirect collaborative filtering (ICF) is a method to recommend non-persistent items such as TV programs based on the preferences of other members in a community. In this paper, the effectiveness of these methods is confirmed through experiments. This online TV recommendation system is currently being used by 230,000 members in Japan. The result of the actual operation is also discussed.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11042-006-0077-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1044869", 
        "issn": [
          "1380-7501", 
          "1573-7721"
        ], 
        "name": "Multimedia Tools and Applications", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1-2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "36"
      }
    ], 
    "name": "Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system", 
    "pagination": "37-54", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6ed2a7a3f436629acc089014812d662f93e2322e0ff164ada77b03b569b098bb"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11042-006-0077-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1023261670"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11042-006-0077-4", 
      "https://app.dimensions.ai/details/publication/pub.1023261670"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:08", 
    "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/v90/0000000001_0000000264/records_8678_00000512.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11042-006-0077-4"
  }
]
 

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/s11042-006-0077-4'

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/s11042-006-0077-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11042-006-0077-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11042-006-0077-4'


 

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

90 TRIPLES      21 PREDICATES      34 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11042-006-0077-4 schema:about anzsrc-for:08
2 anzsrc-for:0806
3 schema:author N9ae470ca3c8e415ea45682c1311cc10c
4 schema:citation sg:pub.10.1007/1-4020-2164-x_1
5 https://doi.org/10.1109/52.582976
6 https://doi.org/10.1109/ccnc.2005.1405145
7 https://doi.org/10.1109/mic.2003.1167344
8 https://doi.org/10.1145/192844.192905
9 https://doi.org/10.1145/358916.358995
10 https://doi.org/10.1145/500141.500238
11 schema:datePublished 2008-01
12 schema:datePublishedReg 2008-01-01
13 schema:description TV Program recommendation is a good example of a novel application of networked appliances using personalization technologies. The aim of this paper is to propose methods to improve the accuracy of TV program recommendation. Automatic metadata expansion (AME) is a method to enhance TV program metadata from electronic program guide (EPG) data, and indirect collaborative filtering (ICF) is a method to recommend non-persistent items such as TV programs based on the preferences of other members in a community. In this paper, the effectiveness of these methods is confirmed through experiments. This online TV recommendation system is currently being used by 230,000 members in Japan. The result of the actual operation is also discussed.
14 schema:genre research_article
15 schema:inLanguage en
16 schema:isAccessibleForFree false
17 schema:isPartOf N5185db8d987047f6b8cd40bca381a28b
18 N710d8d606f5a4211b2f1c2421ebc67ab
19 sg:journal.1044869
20 schema:name Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system
21 schema:pagination 37-54
22 schema:productId N3a086b9b65a54335aaad51f722ac41a0
23 N7a8c96c13e5d47d7bb1d0d517ea8e396
24 Nfcbc9deff88f49fd956a16e7c4365d7f
25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023261670
26 https://doi.org/10.1007/s11042-006-0077-4
27 schema:sdDatePublished 2019-04-10T19:08
28 schema:sdLicense https://scigraph.springernature.com/explorer/license/
29 schema:sdPublisher Nea22512b6df141b1a68ae59a6c72ed87
30 schema:url http://link.springer.com/10.1007%2Fs11042-006-0077-4
31 sgo:license sg:explorer/license/
32 sgo:sdDataset articles
33 rdf:type schema:ScholarlyArticle
34 N14389d5d5bf844c2b333a85007ad31b1 rdf:first sg:person.013746657401.09
35 rdf:rest rdf:nil
36 N3a086b9b65a54335aaad51f722ac41a0 schema:name dimensions_id
37 schema:value pub.1023261670
38 rdf:type schema:PropertyValue
39 N5185db8d987047f6b8cd40bca381a28b schema:issueNumber 1-2
40 rdf:type schema:PublicationIssue
41 N710d8d606f5a4211b2f1c2421ebc67ab schema:volumeNumber 36
42 rdf:type schema:PublicationVolume
43 N7a8c96c13e5d47d7bb1d0d517ea8e396 schema:name doi
44 schema:value 10.1007/s11042-006-0077-4
45 rdf:type schema:PropertyValue
46 N9ae470ca3c8e415ea45682c1311cc10c rdf:first sg:person.012353716401.84
47 rdf:rest N14389d5d5bf844c2b333a85007ad31b1
48 Nea22512b6df141b1a68ae59a6c72ed87 schema:name Springer Nature - SN SciGraph project
49 rdf:type schema:Organization
50 Nfcbc9deff88f49fd956a16e7c4365d7f schema:name readcube_id
51 schema:value 6ed2a7a3f436629acc089014812d662f93e2322e0ff164ada77b03b569b098bb
52 rdf:type schema:PropertyValue
53 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
54 schema:name Information and Computing Sciences
55 rdf:type schema:DefinedTerm
56 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
57 schema:name Information Systems
58 rdf:type schema:DefinedTerm
59 sg:journal.1044869 schema:issn 1380-7501
60 1573-7721
61 schema:name Multimedia Tools and Applications
62 rdf:type schema:Periodical
63 sg:person.012353716401.84 schema:affiliation https://www.grid.ac/institutes/grid.410792.9
64 schema:familyName Tsunoda
65 schema:givenName Tomohiro
66 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012353716401.84
67 rdf:type schema:Person
68 sg:person.013746657401.09 schema:affiliation https://www.grid.ac/institutes/grid.410792.9
69 schema:familyName Hoshino
70 schema:givenName Masaaki
71 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013746657401.09
72 rdf:type schema:Person
73 sg:pub.10.1007/1-4020-2164-x_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024294478
74 https://doi.org/10.1007/1-4020-2164-x_1
75 rdf:type schema:CreativeWork
76 https://doi.org/10.1109/52.582976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061185738
77 rdf:type schema:CreativeWork
78 https://doi.org/10.1109/ccnc.2005.1405145 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093579844
79 rdf:type schema:CreativeWork
80 https://doi.org/10.1109/mic.2003.1167344 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061403307
81 rdf:type schema:CreativeWork
82 https://doi.org/10.1145/192844.192905 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051044947
83 rdf:type schema:CreativeWork
84 https://doi.org/10.1145/358916.358995 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053251882
85 rdf:type schema:CreativeWork
86 https://doi.org/10.1145/500141.500238 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028525891
87 rdf:type schema:CreativeWork
88 https://www.grid.ac/institutes/grid.410792.9 schema:alternateName Sony (Japan)
89 schema:name PAO Group, Intelligent Systems Research Laboratory, Information Technologies Laboratories, Sony Corporation, Tokyo, Japan
90 rdf:type schema:Organization
 




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


...