USER CUSTOMIZATION TYPE COMMODITY RECOMMENDATION DEVICE THROUGH ARTIFICIAL INTELLIGENCE-BASED MACHINE LEARNING
To provide a user customization type commodity recommendation device through artificial intelligence-based machine learning.SOLUTION: A user customization type commodity recommendation devise through artificial intelligence-based machine learning, based on a conversion table into which prespecified...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Patent |
| Sprache: | eng ; jpn |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | LEE DONG HEE SHIN SEONG KI |
| description | To provide a user customization type commodity recommendation device through artificial intelligence-based machine learning.SOLUTION: A user customization type commodity recommendation devise through artificial intelligence-based machine learning, based on a conversion table into which prespecified numerical value is recorded as corresponding to each personal information, converts N-pieces of personal information collected from customers into N-pieces of personal information converted values, and calculates internal product between an M-dimensional calculation vector generated by multiplying a personal information vector containing the N-pieces of personal information converted values as a content by a first weighting matrix and an M-dimensional commodity vector of M-pieces of commodities. Then, based on an already-set activation function, internal product values to each of the M-pieces of commodities are converted to values between "0" and "1", to generate output values to the M-pieces of commodities. A loss value based on an already-set loss function is calculated from the output value to the M-pieces of commodities and a purchase result value, and machine learning for determining the first weighting matrix so that the loss value becomes the minimum.SELECTED DRAWING: Figure 2
【課題】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置を提供する。【解決手段】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置は、各個人情報に対応するものとして予め指定された数値が記録されている変換テーブルに基づいて、顧客から収集したＮ個の個人情報をＮ個の個人情報変換値に変換、Ｎ個の個人情報変換値を成分とする個人情報ベクトルに第１重み行列を乗じて生成したＭ次元の演算ベクトルとＭ個の商品のＭ次元の商品ベクトルとの間の内積を演算する。次に、既に設定された活性化関数に基づきＭ個の商品の各々に対する内積値を「０」と「１」との間の値に変換し、Ｍ個の商品に対する出力値を生成する。Ｍ個の商品に対する出力値と購買結果値から既に設定された損失関数に基づく損失値を演算、損失値が最小になるように第１重み行列を決定する機械学習を実行する。【選択図】図２ |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_JP2021039712A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>JP2021039712A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_JP2021039712A3</originalsourceid><addsrcrecordid>eNqNirsKwkAQANNYiPoPi30gj0Isz7tNbuUe4bIRYhOCnJXEQPx_RPQDrGZgZp1MXYsBZNeyt3QVTN4B9w2C9NZ6RdxDwI-jU9-q8EISgXXwXa1BBKaKJAkD5BiNoRqdxPQkWlRghdTkEAyK4MjV22R1Hx9L3P24SfYVstRpnJ9DXObxFqf4Gs5NkRV5Vh4PeSHKv6Y3OSQ4MA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>USER CUSTOMIZATION TYPE COMMODITY RECOMMENDATION DEVICE THROUGH ARTIFICIAL INTELLIGENCE-BASED MACHINE LEARNING</title><source>esp@cenet</source><creator>LEE DONG HEE ; SHIN SEONG KI</creator><creatorcontrib>LEE DONG HEE ; SHIN SEONG KI</creatorcontrib><description>To provide a user customization type commodity recommendation device through artificial intelligence-based machine learning.SOLUTION: A user customization type commodity recommendation devise through artificial intelligence-based machine learning, based on a conversion table into which prespecified numerical value is recorded as corresponding to each personal information, converts N-pieces of personal information collected from customers into N-pieces of personal information converted values, and calculates internal product between an M-dimensional calculation vector generated by multiplying a personal information vector containing the N-pieces of personal information converted values as a content by a first weighting matrix and an M-dimensional commodity vector of M-pieces of commodities. Then, based on an already-set activation function, internal product values to each of the M-pieces of commodities are converted to values between "0" and "1", to generate output values to the M-pieces of commodities. A loss value based on an already-set loss function is calculated from the output value to the M-pieces of commodities and a purchase result value, and machine learning for determining the first weighting matrix so that the loss value becomes the minimum.SELECTED DRAWING: Figure 2
【課題】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置を提供する。【解決手段】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置は、各個人情報に対応するものとして予め指定された数値が記録されている変換テーブルに基づいて、顧客から収集したＮ個の個人情報をＮ個の個人情報変換値に変換、Ｎ個の個人情報変換値を成分とする個人情報ベクトルに第１重み行列を乗じて生成したＭ次元の演算ベクトルとＭ個の商品のＭ次元の商品ベクトルとの間の内積を演算する。次に、既に設定された活性化関数に基づきＭ個の商品の各々に対する内積値を「０」と「１」との間の値に変換し、Ｍ個の商品に対する出力値を生成する。Ｍ個の商品に対する出力値と購買結果値から既に設定された損失関数に基づく損失値を演算、損失値が最小になるように第１重み行列を決定する機械学習を実行する。【選択図】図２</description><language>eng ; jpn</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2021</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210311&DB=EPODOC&CC=JP&NR=2021039712A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25544,76293</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210311&DB=EPODOC&CC=JP&NR=2021039712A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LEE DONG HEE</creatorcontrib><creatorcontrib>SHIN SEONG KI</creatorcontrib><title>USER CUSTOMIZATION TYPE COMMODITY RECOMMENDATION DEVICE THROUGH ARTIFICIAL INTELLIGENCE-BASED MACHINE LEARNING</title><description>To provide a user customization type commodity recommendation device through artificial intelligence-based machine learning.SOLUTION: A user customization type commodity recommendation devise through artificial intelligence-based machine learning, based on a conversion table into which prespecified numerical value is recorded as corresponding to each personal information, converts N-pieces of personal information collected from customers into N-pieces of personal information converted values, and calculates internal product between an M-dimensional calculation vector generated by multiplying a personal information vector containing the N-pieces of personal information converted values as a content by a first weighting matrix and an M-dimensional commodity vector of M-pieces of commodities. Then, based on an already-set activation function, internal product values to each of the M-pieces of commodities are converted to values between "0" and "1", to generate output values to the M-pieces of commodities. A loss value based on an already-set loss function is calculated from the output value to the M-pieces of commodities and a purchase result value, and machine learning for determining the first weighting matrix so that the loss value becomes the minimum.SELECTED DRAWING: Figure 2
【課題】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置を提供する。【解決手段】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置は、各個人情報に対応するものとして予め指定された数値が記録されている変換テーブルに基づいて、顧客から収集したＮ個の個人情報をＮ個の個人情報変換値に変換、Ｎ個の個人情報変換値を成分とする個人情報ベクトルに第１重み行列を乗じて生成したＭ次元の演算ベクトルとＭ個の商品のＭ次元の商品ベクトルとの間の内積を演算する。次に、既に設定された活性化関数に基づきＭ個の商品の各々に対する内積値を「０」と「１」との間の値に変換し、Ｍ個の商品に対する出力値を生成する。Ｍ個の商品に対する出力値と購買結果値から既に設定された損失関数に基づく損失値を演算、損失値が最小になるように第１重み行列を決定する機械学習を実行する。【選択図】図２</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNirsKwkAQANNYiPoPi30gj0Isz7tNbuUe4bIRYhOCnJXEQPx_RPQDrGZgZp1MXYsBZNeyt3QVTN4B9w2C9NZ6RdxDwI-jU9-q8EISgXXwXa1BBKaKJAkD5BiNoRqdxPQkWlRghdTkEAyK4MjV22R1Hx9L3P24SfYVstRpnJ9DXObxFqf4Gs5NkRV5Vh4PeSHKv6Y3OSQ4MA</recordid><startdate>20210311</startdate><enddate>20210311</enddate><creator>LEE DONG HEE</creator><creator>SHIN SEONG KI</creator><scope>EVB</scope></search><sort><creationdate>20210311</creationdate><title>USER CUSTOMIZATION TYPE COMMODITY RECOMMENDATION DEVICE THROUGH ARTIFICIAL INTELLIGENCE-BASED MACHINE LEARNING</title><author>LEE DONG HEE ; SHIN SEONG KI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_JP2021039712A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; jpn</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>LEE DONG HEE</creatorcontrib><creatorcontrib>SHIN SEONG KI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LEE DONG HEE</au><au>SHIN SEONG KI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>USER CUSTOMIZATION TYPE COMMODITY RECOMMENDATION DEVICE THROUGH ARTIFICIAL INTELLIGENCE-BASED MACHINE LEARNING</title><date>2021-03-11</date><risdate>2021</risdate><abstract>To provide a user customization type commodity recommendation device through artificial intelligence-based machine learning.SOLUTION: A user customization type commodity recommendation devise through artificial intelligence-based machine learning, based on a conversion table into which prespecified numerical value is recorded as corresponding to each personal information, converts N-pieces of personal information collected from customers into N-pieces of personal information converted values, and calculates internal product between an M-dimensional calculation vector generated by multiplying a personal information vector containing the N-pieces of personal information converted values as a content by a first weighting matrix and an M-dimensional commodity vector of M-pieces of commodities. Then, based on an already-set activation function, internal product values to each of the M-pieces of commodities are converted to values between "0" and "1", to generate output values to the M-pieces of commodities. A loss value based on an already-set loss function is calculated from the output value to the M-pieces of commodities and a purchase result value, and machine learning for determining the first weighting matrix so that the loss value becomes the minimum.SELECTED DRAWING: Figure 2
【課題】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置を提供する。【解決手段】人工知能ベースの機械学習を通じたユーザカスタマイズ型商品推薦装置は、各個人情報に対応するものとして予め指定された数値が記録されている変換テーブルに基づいて、顧客から収集したＮ個の個人情報をＮ個の個人情報変換値に変換、Ｎ個の個人情報変換値を成分とする個人情報ベクトルに第１重み行列を乗じて生成したＭ次元の演算ベクトルとＭ個の商品のＭ次元の商品ベクトルとの間の内積を演算する。次に、既に設定された活性化関数に基づきＭ個の商品の各々に対する内積値を「０」と「１」との間の値に変換し、Ｍ個の商品に対する出力値を生成する。Ｍ個の商品に対する出力値と購買結果値から既に設定された損失関数に基づく損失値を演算、損失値が最小になるように第１重み行列を決定する機械学習を実行する。【選択図】図２</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng ; jpn |
| recordid | cdi_epo_espacenet_JP2021039712A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | USER CUSTOMIZATION TYPE COMMODITY RECOMMENDATION DEVICE THROUGH ARTIFICIAL INTELLIGENCE-BASED MACHINE LEARNING |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T14%3A02%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LEE%20DONG%20HEE&rft.date=2021-03-11&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EJP2021039712A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |