Just ask: designing intent driven algos
"Presented by Anna Schneider, Data Science Manager at Stitch Fix. Classic recommender systems are great for answering the question 'what does a user want in general?'. However, they only get you partway to an answer to 'what does a user want right now?'. To close the gap, it...
Gespeichert in:
| Weitere beteiligte Personen: | |
|---|---|
| Format: | Elektronisch Video |
| Sprache: | Englisch |
| Veröffentlicht: |
[Seattle, Washington]
Data Science Salon
2019
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/00000BOWAG8SEXPG/?ar |
| Zusammenfassung: | "Presented by Anna Schneider, Data Science Manager at Stitch Fix. Classic recommender systems are great for answering the question 'what does a user want in general?'. However, they only get you partway to an answer to 'what does a user want right now?'. To close the gap, it helps to capture and act on real-time user intent. I'll share two examples of this paradigm, and the resulting changes to our algorithms and architectures at Stitch Fix."--Resource description page |
| Beschreibung: | Title from resource description page (Safari, viewed October 29, 2020). - Place of publication from title screen |
| Umfang: | 1 Online-Ressource (1 streaming video file (25 min., 36 sec.)) |
Internformat
MARC
| LEADER | 00000cgm a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-056582412 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228121207.0 | ||
| 006 | m o | | | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 200916s2019 xx ||| |o o ||eng c | ||
| 035 | |a (DE-627-1)056582412 | ||
| 035 | |a (DE-599)KEP056582412 | ||
| 035 | |a (ORHE)00000BOWAG8SEXPG | ||
| 035 | |a (DE-627-1)056582412 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 245 | 1 | 0 | |a Just ask |b designing intent driven algos |c Data Science Salon |
| 264 | 1 | |a [Seattle, Washington] |b Data Science Salon |c 2019 | |
| 300 | |a 1 Online-Ressource (1 streaming video file (25 min., 36 sec.)) | ||
| 336 | |a zweidimensionales bewegtes Bild |b tdi |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Title from resource description page (Safari, viewed October 29, 2020). - Place of publication from title screen | ||
| 520 | |a "Presented by Anna Schneider, Data Science Manager at Stitch Fix. Classic recommender systems are great for answering the question 'what does a user want in general?'. However, they only get you partway to an answer to 'what does a user want right now?'. To close the gap, it helps to capture and act on real-time user intent. I'll share two examples of this paradigm, and the resulting changes to our algorithms and architectures at Stitch Fix."--Resource description page | ||
| 650 | 0 | |a Computer algorithms | |
| 650 | 0 | |a Consumers' preferences | |
| 650 | 0 | |a Computer network architectures | |
| 650 | 0 | |a Information technology |x Management | |
| 650 | 0 | |a Electronic commerce | |
| 650 | 0 | |a Algorithms | |
| 650 | 4 | |a Algorithmes | |
| 650 | 4 | |a Consommateurs ; Préférences | |
| 650 | 4 | |a Réseaux d'ordinateurs ; Architectures | |
| 650 | 4 | |a Technologie de l'information ; Gestion | |
| 650 | 4 | |a Commerce électronique | |
| 650 | 4 | |a algorithms | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 4 | |a Computer network architectures | |
| 650 | 4 | |a Consumers' preferences | |
| 650 | 4 | |a Electronic commerce | |
| 650 | 4 | |a Information technology ; Management | |
| 700 | 1 | |a Schneider, Anna |e MitwirkendeR |4 ctb | |
| 710 | 2 | |a Data Science Salon, |e Verlag |4 pbl | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/00000BOWAG8SEXPG/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 935 | |c vide | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-056582412 |
|---|---|
| _version_ | 1821494946586165248 |
| adam_text | |
| any_adam_object | |
| author2 | Schneider, Anna |
| author2_role | ctb |
| author2_variant | a s as |
| author_facet | Schneider, Anna |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)056582412 (DE-599)KEP056582412 (ORHE)00000BOWAG8SEXPG |
| format | Electronic Video |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02426cgm a22005532 4500</leader><controlfield tag="001">ZDB-30-ORH-056582412</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121207.0</controlfield><controlfield tag="006">m o | | </controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">200916s2019 xx ||| |o o ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)056582412</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP056582412</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)00000BOWAG8SEXPG</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)056582412</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Just ask</subfield><subfield code="b">designing intent driven algos</subfield><subfield code="c">Data Science Salon</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Seattle, Washington]</subfield><subfield code="b">Data Science Salon</subfield><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 streaming video file (25 min., 36 sec.))</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">zweidimensionales bewegtes Bild</subfield><subfield code="b">tdi</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Title from resource description page (Safari, viewed October 29, 2020). - Place of publication from title screen</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"Presented by Anna Schneider, Data Science Manager at Stitch Fix. Classic recommender systems are great for answering the question 'what does a user want in general?'. However, they only get you partway to an answer to 'what does a user want right now?'. To close the gap, it helps to capture and act on real-time user intent. I'll share two examples of this paradigm, and the resulting changes to our algorithms and architectures at Stitch Fix."--Resource description page</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Computer algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Consumers' preferences</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Computer network architectures</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Information technology</subfield><subfield code="x">Management</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Electronic commerce</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithmes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Consommateurs ; Préférences</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Réseaux d'ordinateurs ; Architectures</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Technologie de l'information ; Gestion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Commerce électronique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer network architectures</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Consumers' preferences</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electronic commerce</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information technology ; Management</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schneider, Anna</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">Data Science Salon,</subfield><subfield code="e">Verlag</subfield><subfield code="4">pbl</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/00000BOWAG8SEXPG/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="935" ind1=" " ind2=" "><subfield code="c">vide</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-056582412 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:22:27Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (1 streaming video file (25 min., 36 sec.)) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Data Science Salon |
| record_format | marc |
| spelling | Just ask designing intent driven algos Data Science Salon [Seattle, Washington] Data Science Salon 2019 1 Online-Ressource (1 streaming video file (25 min., 36 sec.)) zweidimensionales bewegtes Bild tdi rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Title from resource description page (Safari, viewed October 29, 2020). - Place of publication from title screen "Presented by Anna Schneider, Data Science Manager at Stitch Fix. Classic recommender systems are great for answering the question 'what does a user want in general?'. However, they only get you partway to an answer to 'what does a user want right now?'. To close the gap, it helps to capture and act on real-time user intent. I'll share two examples of this paradigm, and the resulting changes to our algorithms and architectures at Stitch Fix."--Resource description page Computer algorithms Consumers' preferences Computer network architectures Information technology Management Electronic commerce Algorithms Algorithmes Consommateurs ; Préférences Réseaux d'ordinateurs ; Architectures Technologie de l'information ; Gestion Commerce électronique algorithms Information technology ; Management Schneider, Anna MitwirkendeR ctb Data Science Salon, Verlag pbl |
| spellingShingle | Just ask designing intent driven algos Computer algorithms Consumers' preferences Computer network architectures Information technology Management Electronic commerce Algorithms Algorithmes Consommateurs ; Préférences Réseaux d'ordinateurs ; Architectures Technologie de l'information ; Gestion Commerce électronique algorithms Information technology ; Management |
| title | Just ask designing intent driven algos |
| title_auth | Just ask designing intent driven algos |
| title_exact_search | Just ask designing intent driven algos |
| title_full | Just ask designing intent driven algos Data Science Salon |
| title_fullStr | Just ask designing intent driven algos Data Science Salon |
| title_full_unstemmed | Just ask designing intent driven algos Data Science Salon |
| title_short | Just ask |
| title_sort | just ask designing intent driven algos |
| title_sub | designing intent driven algos |
| topic | Computer algorithms Consumers' preferences Computer network architectures Information technology Management Electronic commerce Algorithms Algorithmes Consommateurs ; Préférences Réseaux d'ordinateurs ; Architectures Technologie de l'information ; Gestion Commerce électronique algorithms Information technology ; Management |
| topic_facet | Computer algorithms Consumers' preferences Computer network architectures Information technology Management Electronic commerce Algorithms Algorithmes Consommateurs ; Préférences Réseaux d'ordinateurs ; Architectures Technologie de l'information ; Gestion Commerce électronique algorithms Information technology ; Management |
| work_keys_str_mv | AT schneideranna justaskdesigningintentdrivenalgos AT datasciencesalon justaskdesigningintentdrivenalgos |