Collaborative filtering utilizing a belief network


Ontology type: sgo:Patent     


Patent Info

DATE

1997-12-30T00:00

AUTHORS

David E. Heckerman , John S. Breese , Eric Horvitz , David Maxwell Chickering

ABSTRACT

The disclosed system provides an improved collaborative filtering system by utilizing a belief network, which is sometimes known as a Bayesian network. The disclosed system learns a belief network using both prior knowledge obtained from an expert in a given field of decision making and a database containing empirical data obtained from many people. The empirical data contains attributes of users as well as their preferences in the field of decision making. After initially learning the belief network, the belief network is relearned at various intervals when additional attributes are identified as having a causal effect on the preferences and data for these additional attributes can be gathered. This relearning allows the belief network to improve its accuracy at predicting preferences of a user. Upon each iteration of relearning, a cluster model is automatically generated that best predicts the data in the database. After relearning the belief network a number of times, the belief network is used to predict the preferences of a user using probabilistic inference. In performing probabilistic inference, the known attributes of a user are received and the belief network is accessed to determine the probability of the unknown preferences of the user given the known attributes. Based on these probabilities, the preference most likely to be desired by the user can be predicted. More... »

Related SciGraph Publications

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/2008/2790", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2358", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "name": "David E. Heckerman", 
        "type": "Person"
      }, 
      {
        "name": "John S. Breese", 
        "type": "Person"
      }, 
      {
        "name": "Eric Horvitz", 
        "type": "Person"
      }, 
      {
        "name": "David Maxwell Chickering", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1145/138859.138867", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005134077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00994110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046316965", 
          "https://doi.org/10.1007/bf00994110"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1997-12-30T00:00", 
    "description": "

The disclosed system provides an improved collaborative filtering system by utilizing a belief network, which is sometimes known as a Bayesian network. The disclosed system learns a belief network using both prior knowledge obtained from an expert in a given field of decision making and a database containing empirical data obtained from many people. The empirical data contains attributes of users as well as their preferences in the field of decision making. After initially learning the belief network, the belief network is relearned at various intervals when additional attributes are identified as having a causal effect on the preferences and data for these additional attributes can be gathered. This relearning allows the belief network to improve its accuracy at predicting preferences of a user. Upon each iteration of relearning, a cluster model is automatically generated that best predicts the data in the database. After relearning the belief network a number of times, the belief network is used to predict the preferences of a user using probabilistic inference. In performing probabilistic inference, the known attributes of a user are received and the belief network is accessed to determine the probability of the unknown preferences of the user given the known attributes. Based on these probabilities, the preference most likely to be desired by the user can be predicted.

", "id": "sg:patent.US-5704017-A", "keywords": [ "belief network", "Bayesian network", "learns", "prior knowledge", "expert", "given field", "database", "empirical data", "attribute", "user", "preference", "interval", "causal effect", "accuracy", "iteration", "cluster", "probabilistic inference", "probability" ], "name": "Collaborative filtering utilizing a belief network", "recipient": [ { "id": "https://www.grid.ac/institutes/grid.419815.0", "type": "Organization" } ], "sameAs": [ "https://app.dimensions.ai/details/patent/US-5704017-A" ], "sdDataset": "patents", "sdDatePublished": "2019-04-18T10:26", "sdLicense": "https://scigraph.springernature.com/explorer/license/", "sdPublisher": { "name": "Springer Nature - SN SciGraph project", "type": "Organization" }, "sdSource": "s3://com-uberresearch-data-patents-target-20190320-rc/data/sn-export/402f166718b70575fb5d4ffe01f064d1/0000100128-0000352499/json_export_03038.jsonl", "type": "Patent" } ]
 

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/patent.US-5704017-A'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.US-5704017-A'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.US-5704017-A'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.US-5704017-A'


 

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

62 TRIPLES      15 PREDICATES      35 URIs      26 LITERALS      2 BLANK NODES

Subject Predicate Object
1 sg:patent.US-5704017-A schema:about anzsrc-for:2358
2 anzsrc-for:2790
3 schema:author N7244b287af764d22926ea290996dd9f7
4 schema:citation sg:pub.10.1007/bf00994110
5 https://doi.org/10.1145/138859.138867
6 schema:datePublished 1997-12-30T00:00
7 schema:description <p>The disclosed system provides an improved collaborative filtering system by utilizing a belief network, which is sometimes known as a Bayesian network. The disclosed system learns a belief network using both prior knowledge obtained from an expert in a given field of decision making and a database containing empirical data obtained from many people. The empirical data contains attributes of users as well as their preferences in the field of decision making. After initially learning the belief network, the belief network is relearned at various intervals when additional attributes are identified as having a causal effect on the preferences and data for these additional attributes can be gathered. This relearning allows the belief network to improve its accuracy at predicting preferences of a user. Upon each iteration of relearning, a cluster model is automatically generated that best predicts the data in the database. After relearning the belief network a number of times, the belief network is used to predict the preferences of a user using probabilistic inference. In performing probabilistic inference, the known attributes of a user are received and the belief network is accessed to determine the probability of the unknown preferences of the user given the known attributes. Based on these probabilities, the preference most likely to be desired by the user can be predicted.</p>
8 schema:keywords Bayesian network
9 accuracy
10 attribute
11 belief network
12 causal effect
13 cluster
14 database
15 empirical data
16 expert
17 given field
18 interval
19 iteration
20 learns
21 preference
22 prior knowledge
23 probabilistic inference
24 probability
25 user
26 schema:name Collaborative filtering utilizing a belief network
27 schema:recipient https://www.grid.ac/institutes/grid.419815.0
28 schema:sameAs https://app.dimensions.ai/details/patent/US-5704017-A
29 schema:sdDatePublished 2019-04-18T10:26
30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
31 schema:sdPublisher N96808b8d202c4b6299da5dc79d775510
32 sgo:license sg:explorer/license/
33 sgo:sdDataset patents
34 rdf:type sgo:Patent
35 N10eb14cd09bc4496bd2e0f1a9427eb21 rdf:first Ne987e44514ac4a4595d4e60a4c0e8c7b
36 rdf:rest rdf:nil
37 N6b5708eb63dd44fd9858bfd007d2ab84 rdf:first Nfa84740e8ab142788d592b5a71251cc9
38 rdf:rest Nf0cca1b04ed640a1a3f62d2faf41a746
39 N710241599bae4a93a08034c0a51e9683 schema:name David E. Heckerman
40 rdf:type schema:Person
41 N7244b287af764d22926ea290996dd9f7 rdf:first N710241599bae4a93a08034c0a51e9683
42 rdf:rest N6b5708eb63dd44fd9858bfd007d2ab84
43 N96808b8d202c4b6299da5dc79d775510 schema:name Springer Nature - SN SciGraph project
44 rdf:type schema:Organization
45 Ncae0119aa1954e4ca2972f79c2969cb6 schema:name Eric Horvitz
46 rdf:type schema:Person
47 Ne987e44514ac4a4595d4e60a4c0e8c7b schema:name David Maxwell Chickering
48 rdf:type schema:Person
49 Nf0cca1b04ed640a1a3f62d2faf41a746 rdf:first Ncae0119aa1954e4ca2972f79c2969cb6
50 rdf:rest N10eb14cd09bc4496bd2e0f1a9427eb21
51 Nfa84740e8ab142788d592b5a71251cc9 schema:name John S. Breese
52 rdf:type schema:Person
53 anzsrc-for:2358 schema:inDefinedTermSet anzsrc-for:
54 rdf:type schema:DefinedTerm
55 anzsrc-for:2790 schema:inDefinedTermSet anzsrc-for:
56 rdf:type schema:DefinedTerm
57 sg:pub.10.1007/bf00994110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046316965
58 https://doi.org/10.1007/bf00994110
59 rdf:type schema:CreativeWork
60 https://doi.org/10.1145/138859.138867 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005134077
61 rdf:type schema:CreativeWork
62 https://www.grid.ac/institutes/grid.419815.0 schema:Organization
 




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


...