東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Fashion recommender systems

Workshop on Recommender Systems in Fashion (2019 :)

Linked to FindBook

Google Book

Amazon

博客來

Fashion recommender systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fashion recommender systems/ edited by Nima Dokoohaki.
other author:	Dokoohaki, Nima.
corporate name:	Workshop on Recommender Systems in Fashion
Published:	Cham :Springer International Publishing : : 2020.,
Description:	vii, 145 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Part 1. Cold Start in Recommendations -- Chapter 1. Fashion Recommender Systems in Cold Start ( Mehdi Elahi) -- Part 2. Complementary and Session Based Recommendation -- Chapter 2. Enabling Hyper-Personalisation: Automated AdCreative Generation and Ranking for Fashion e-Commerce (Sreekanth Vempati) -- Chapter 3. Two-Stage Session-based Recommendations with Candidate Rank Embeddings (Jose Antonio Sanchez Rodrguez) -- Part 3. Outfit Recommendations -- Chapter 4. Attention-based Fusion for Outfit Recommendation (Katrien Laenen) -- Chapter 5. Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits' Recommendation (Shatha Jaradat) -- Part 4. Sizing and Fit Recommendations -- Chapter 6. Learning Size and Fit from Fashion Images (Nour Karessli) -- Part 5. Generative Outfit Recommendation -- Chapter 7. Generating High-Resolution Fashion Model Images Wearing Custom Outfits (Gokhan Yildirim)
Contained By:	Springer Nature eBook
Subject:	Recommender systems (Information filtering) - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-030-55218-3
ISBN:	9783030552183

Fashion recommender systems
Fashion recommender systems[electronic resource] /edited by Nima Dokoohaki. - Cham :Springer International Publishing :2020. - vii, 145 p. :ill. (some col.), digital ;24 cm. - Lecture notes in social networks,2190-5428. - Lecture notes in social networks..

Part 1. Cold Start in Recommendations -- Chapter 1. Fashion Recommender Systems in Cold Start ( Mehdi Elahi) -- Part 2. Complementary and Session Based Recommendation -- Chapter 2. Enabling Hyper-Personalisation: Automated AdCreative Generation and Ranking for Fashion e-Commerce (Sreekanth Vempati) -- Chapter 3. Two-Stage Session-based Recommendations with Candidate Rank Embeddings (Jose Antonio Sanchez Rodrguez) -- Part 3. Outfit Recommendations -- Chapter 4. Attention-based Fusion for Outfit Recommendation (Katrien Laenen) -- Chapter 5. Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits' Recommendation (Shatha Jaradat) -- Part 4. Sizing and Fit Recommendations -- Chapter 6. Learning Size and Fit from Fashion Images (Nour Karessli) -- Part 5. Generative Outfit Recommendation -- Chapter 7. Generating High-Resolution Fashion Model Images Wearing Custom Outfits (Gokhan Yildirim)

This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers' social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.

ISBN: 9783030552183

Standard No.: 10.1007/978-3-030-55218-3doiSubjects--Topical Terms:

3221329
Recommender systems (Information filtering)
--Congresses.

LC Class. No.: ZA3084

Dewey Class. No.: 025.04

Fashion recommender systems
LDR:04581nmm a2200349 a 4500 001 2257142
003 DE-He213
005 20210311140909.0
006 m d
007 cr nn 008maaau
008 220420s2020 sz s 0 eng d
020 $a 9783030552183 $q (electronic bk.)
020 $a 9783030552176 $q (paper)
024 7 $a 10.1007/978-3-030-55218-3 $2 doi
035 $a 978-3-030-55218-3
040 $a GP $c GP
041 0 $a eng
050 4 $a ZA3084
072 7 $a PHU $2 bicssc
072 7 $a SCI064000 $2 bisacsh
072 7 $a PHU $2 thema
072 7 $a PBKD $2 thema
082 0 4 $a 025.04 $2 23
090 $a ZA3084 $b .W926 2019
111 2 $a Workshop on Recommender Systems in Fashion $n (1st : $d 2019 : $c Copenhagen, Denmark) $3 3528085
245 1 0 $a Fashion recommender systems $h [electronic resource] / $c edited by Nima Dokoohaki.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a vii, 145 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in social networks, $x 2190-5428
505 0 $a Part 1. Cold Start in Recommendations -- Chapter 1. Fashion Recommender Systems in Cold Start ( Mehdi Elahi) -- Part 2. Complementary and Session Based Recommendation -- Chapter 2. Enabling Hyper-Personalisation: Automated AdCreative Generation and Ranking for Fashion e-Commerce (Sreekanth Vempati) -- Chapter 3. Two-Stage Session-based Recommendations with Candidate Rank Embeddings (Jose Antonio Sanchez Rodrguez) -- Part 3. Outfit Recommendations -- Chapter 4. Attention-based Fusion for Outfit Recommendation (Katrien Laenen) -- Chapter 5. Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits' Recommendation (Shatha Jaradat) -- Part 4. Sizing and Fit Recommendations -- Chapter 6. Learning Size and Fit from Fashion Images (Nour Karessli) -- Part 5. Generative Outfit Recommendation -- Chapter 7. Generating High-Resolution Fashion Model Images Wearing Custom Outfits (Gokhan Yildirim)
520 $a This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers' social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.
650 0 $a Recommender systems (Information filtering) $v Congresses. $3 3221329
650 0 $a Fashion merchandising $x Data processing $v Congresses. $3 3528086
650 0 $a Human-computer interaction $x Congresses. $3 705966
650 0 $a Machine learning $x Congresses. $3 576368
650 0 $a Information retrieval $v Congresses. $3 884379
650 1 4 $a Applications of Graph Theory and Complex Networks. $3 3134760
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Computational Social Sciences. $3 3220598
650 2 4 $a Signal, Image and Speech Processing. $3 891073
700 1 $a Dokoohaki, Nima. $3 3380391
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in social networks. $3 2058983
856 4 0 $u https://doi.org/10.1007/978-3-030-55218-3
950 $a Mathematics and Statistics (SpringerNature-11649)