Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2003

AUTHORS

Stefan Holland , Martin Ester , Werner Kießling

ABSTRACT

Advanced personalized e-applications require comprehensive knowledge about their user’s likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present novel Preference Mining techniques for detecting strict partial order preferences in user log data. The main advantage of our approach is the semantic expressiveness of the Preference Mining results. Experimental evaluations prove the effectiveness and efficiency of our algorithms. Since the Preference Mining implementation uses sophisticated SQL statements to execute all data-intensive operations on database layer, our algorithms scale well even for large log data sets. With our approach personalized e-applications can gain valuable knowledge about their customers’ preferences, which is essential for a qualified customer service. More... »

PAGES

204-216

References to SciGraph publications

Book

TITLE

Knowledge Discovery in Databases: PKDD 2003

ISBN

978-3-540-20085-7
978-3-540-39804-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-39804-2_20

DOI

http://dx.doi.org/10.1007/978-3-540-39804-2_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002078330


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": "University of Augsburg", 
          "id": "https://www.grid.ac/institutes/grid.7307.3", 
          "name": [
            "Institute of Computer Science, University of Augsburg, D-86159, Augsburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Holland", 
        "givenName": "Stefan", 
        "id": "sg:person.012133752545.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012133752545.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Simon Fraser University", 
          "id": "https://www.grid.ac/institutes/grid.61971.38", 
          "name": [
            "School of Computer Science, Simon Fraser University, V5A 1S6, Burnaby BC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ester", 
        "givenName": "Martin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Augsburg", 
          "id": "https://www.grid.ac/institutes/grid.7307.3", 
          "name": [
            "Institute of Computer Science, University of Augsburg, D-86159, Augsburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kie\u00dfling", 
        "givenName": "Werner", 
        "id": "sg:person.07355710125.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07355710125.73"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1145/775047.775067", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002194678"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/502512.502518", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014566950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/345124.345169", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017767264"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-155860869-6/50098-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019029851"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/347090.347176", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019143234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/371920.372069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020963273"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1013284820704", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021249372", 
          "https://doi.org/10.1023/a:1013284820704"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-45705-4_26", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030398654", 
          "https://doi.org/10.1007/3-540-45705-4_26"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0377-0427(87)90125-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041584630"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-155860869-6/50035-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050593690"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2003", 
    "datePublishedReg": "2003-01-01", 
    "description": "Advanced personalized e-applications require comprehensive knowledge about their user\u2019s likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product offers. In our approach we model such preferences as strict partial orders with \u201cA is better than B\u201d semantics, which has been proven to be very suitable in various e-applications. In this paper we present novel Preference Mining techniques for detecting strict partial order preferences in user log data. The main advantage of our approach is the semantic expressiveness of the Preference Mining results. Experimental evaluations prove the effectiveness and efficiency of our algorithms. Since the Preference Mining implementation uses sophisticated SQL statements to execute all data-intensive operations on database layer, our algorithms scale well even for large log data sets. With our approach personalized e-applications can gain valuable knowledge about their customers\u2019 preferences, which is essential for a qualified customer service.", 
    "editor": [
      {
        "familyName": "Lavra\u010d", 
        "givenName": "Nada", 
        "type": "Person"
      }, 
      {
        "familyName": "Gamberger", 
        "givenName": "Dragan", 
        "type": "Person"
      }, 
      {
        "familyName": "Todorovski", 
        "givenName": "Ljup\u010do", 
        "type": "Person"
      }, 
      {
        "familyName": "Blockeel", 
        "givenName": "Hendrik", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-39804-2_20", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-20085-7", 
        "978-3-540-39804-2"
      ], 
      "name": "Knowledge Discovery in Databases: PKDD 2003", 
      "type": "Book"
    }, 
    "name": "Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications", 
    "pagination": "204-216", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1002078330"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-39804-2_20"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "09e5cc7e3a02617634e4f34a3e91e7288e2d3f8fbf653b0d425fe936c9e691c9"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-39804-2_20", 
      "https://app.dimensions.ai/details/publication/pub.1002078330"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T08:02", 
    "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/v90/0000000359_0000000359/records_29200_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-540-39804-2_20"
  }
]
 

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.10.1007/978-3-540-39804-2_20'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-39804-2_20'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-39804-2_20'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-39804-2_20'


 

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

128 TRIPLES      23 PREDICATES      37 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-39804-2_20 schema:about anzsrc-for:08
2 anzsrc-for:0806
3 schema:author N9a34ba5ea03743a593475c9f27937417
4 schema:citation sg:pub.10.1007/3-540-45705-4_26
5 sg:pub.10.1023/a:1013284820704
6 https://doi.org/10.1016/0377-0427(87)90125-7
7 https://doi.org/10.1016/b978-155860869-6/50035-4
8 https://doi.org/10.1016/b978-155860869-6/50098-6
9 https://doi.org/10.1145/345124.345169
10 https://doi.org/10.1145/347090.347176
11 https://doi.org/10.1145/371920.372069
12 https://doi.org/10.1145/502512.502518
13 https://doi.org/10.1145/775047.775067
14 schema:datePublished 2003
15 schema:datePublishedReg 2003-01-01
16 schema:description Advanced personalized e-applications require comprehensive knowledge about their user’s likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present novel Preference Mining techniques for detecting strict partial order preferences in user log data. The main advantage of our approach is the semantic expressiveness of the Preference Mining results. Experimental evaluations prove the effectiveness and efficiency of our algorithms. Since the Preference Mining implementation uses sophisticated SQL statements to execute all data-intensive operations on database layer, our algorithms scale well even for large log data sets. With our approach personalized e-applications can gain valuable knowledge about their customers’ preferences, which is essential for a qualified customer service.
17 schema:editor N03bc3b44dd64425fb51972bf2cc4075d
18 schema:genre chapter
19 schema:inLanguage en
20 schema:isAccessibleForFree true
21 schema:isPartOf N21c46ba2d79f47128e6e3480e622499f
22 schema:name Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications
23 schema:pagination 204-216
24 schema:productId N5e21909f27ad4a99b6797fb4aadb9524
25 N82fe15b454604e33a856b9dd93e1d63b
26 Na13e5cf28ee74f0c9bd8f5d8a1c28dad
27 schema:publisher N9a5aeb305bfa4b92952d8eb8c45dd0d5
28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002078330
29 https://doi.org/10.1007/978-3-540-39804-2_20
30 schema:sdDatePublished 2019-04-16T08:02
31 schema:sdLicense https://scigraph.springernature.com/explorer/license/
32 schema:sdPublisher N884b7e69e1c9463ca5b9f37abbeef244
33 schema:url https://link.springer.com/10.1007%2F978-3-540-39804-2_20
34 sgo:license sg:explorer/license/
35 sgo:sdDataset chapters
36 rdf:type schema:Chapter
37 N03bc3b44dd64425fb51972bf2cc4075d rdf:first N9430a104904e4d62a2e8595f63e04b22
38 rdf:rest N8f5efd2fe68947298a586921eec73355
39 N0bf7ee232d1e4974acfee37f5c0c46fc rdf:first N318edd8addf643c187aa8e8c4ed14004
40 rdf:rest rdf:nil
41 N21c46ba2d79f47128e6e3480e622499f schema:isbn 978-3-540-20085-7
42 978-3-540-39804-2
43 schema:name Knowledge Discovery in Databases: PKDD 2003
44 rdf:type schema:Book
45 N318edd8addf643c187aa8e8c4ed14004 schema:familyName Blockeel
46 schema:givenName Hendrik
47 rdf:type schema:Person
48 N5d7c4ee0027e41458ba4e954f2d1c9f0 schema:familyName Todorovski
49 schema:givenName Ljupčo
50 rdf:type schema:Person
51 N5e21909f27ad4a99b6797fb4aadb9524 schema:name readcube_id
52 schema:value 09e5cc7e3a02617634e4f34a3e91e7288e2d3f8fbf653b0d425fe936c9e691c9
53 rdf:type schema:PropertyValue
54 N82fe15b454604e33a856b9dd93e1d63b schema:name dimensions_id
55 schema:value pub.1002078330
56 rdf:type schema:PropertyValue
57 N87fe0776e7b541259785e08d62ad10d1 rdf:first N5d7c4ee0027e41458ba4e954f2d1c9f0
58 rdf:rest N0bf7ee232d1e4974acfee37f5c0c46fc
59 N884b7e69e1c9463ca5b9f37abbeef244 schema:name Springer Nature - SN SciGraph project
60 rdf:type schema:Organization
61 N8c215e9cf9c344cf870e61ca91d1d448 rdf:first sg:person.07355710125.73
62 rdf:rest rdf:nil
63 N8f5efd2fe68947298a586921eec73355 rdf:first Na7eb2cf259454d41bcb1bd56fb85f1fd
64 rdf:rest N87fe0776e7b541259785e08d62ad10d1
65 N9430a104904e4d62a2e8595f63e04b22 schema:familyName Lavrač
66 schema:givenName Nada
67 rdf:type schema:Person
68 N9a34ba5ea03743a593475c9f27937417 rdf:first sg:person.012133752545.81
69 rdf:rest Nea18dc0c7d34418cba189c0aa2a60cad
70 N9a5aeb305bfa4b92952d8eb8c45dd0d5 schema:location Berlin, Heidelberg
71 schema:name Springer Berlin Heidelberg
72 rdf:type schema:Organisation
73 Na13e5cf28ee74f0c9bd8f5d8a1c28dad schema:name doi
74 schema:value 10.1007/978-3-540-39804-2_20
75 rdf:type schema:PropertyValue
76 Na7eb2cf259454d41bcb1bd56fb85f1fd schema:familyName Gamberger
77 schema:givenName Dragan
78 rdf:type schema:Person
79 Nea18dc0c7d34418cba189c0aa2a60cad rdf:first Nf6298c38553840b1be742c068d0c0dfb
80 rdf:rest N8c215e9cf9c344cf870e61ca91d1d448
81 Nf6298c38553840b1be742c068d0c0dfb schema:affiliation https://www.grid.ac/institutes/grid.61971.38
82 schema:familyName Ester
83 schema:givenName Martin
84 rdf:type schema:Person
85 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
86 schema:name Information and Computing Sciences
87 rdf:type schema:DefinedTerm
88 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
89 schema:name Information Systems
90 rdf:type schema:DefinedTerm
91 sg:person.012133752545.81 schema:affiliation https://www.grid.ac/institutes/grid.7307.3
92 schema:familyName Holland
93 schema:givenName Stefan
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012133752545.81
95 rdf:type schema:Person
96 sg:person.07355710125.73 schema:affiliation https://www.grid.ac/institutes/grid.7307.3
97 schema:familyName Kießling
98 schema:givenName Werner
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07355710125.73
100 rdf:type schema:Person
101 sg:pub.10.1007/3-540-45705-4_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030398654
102 https://doi.org/10.1007/3-540-45705-4_26
103 rdf:type schema:CreativeWork
104 sg:pub.10.1023/a:1013284820704 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021249372
105 https://doi.org/10.1023/a:1013284820704
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/0377-0427(87)90125-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041584630
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/b978-155860869-6/50035-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050593690
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/b978-155860869-6/50098-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019029851
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1145/345124.345169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017767264
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1145/347090.347176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019143234
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1145/371920.372069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020963273
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1145/502512.502518 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014566950
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1145/775047.775067 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002194678
122 rdf:type schema:CreativeWork
123 https://www.grid.ac/institutes/grid.61971.38 schema:alternateName Simon Fraser University
124 schema:name School of Computer Science, Simon Fraser University, V5A 1S6, Burnaby BC, Canada
125 rdf:type schema:Organization
126 https://www.grid.ac/institutes/grid.7307.3 schema:alternateName University of Augsburg
127 schema:name Institute of Computer Science, University of Augsburg, D-86159, Augsburg, Germany
128 rdf:type schema:Organization
 




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


...