001461471 000__ 04119cam\a22006737i\4500
001461471 001__ 1461471
001461471 003__ OCoLC
001461471 005__ 20230503003354.0
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001461471 008__ 230316s2023\\\\sz\a\\\\o\\\\\100\0\eng\d
001461471 019__ $$a1371686422$$a1372396403
001461471 020__ $$a9783031221927$$q(electronic bk.)
001461471 020__ $$a3031221923$$q(electronic bk.)
001461471 020__ $$z9783031221910
001461471 020__ $$z3031221915
001461471 0247_ $$a10.1007/978-3-031-22192-7$$2doi
001461471 035__ $$aSP(OCoLC)1373208863
001461471 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dUKAHL$$dOCLCF
001461471 049__ $$aISEA
001461471 050_4 $$aZA3084
001461471 08204 $$a005.5/6$$223/eng/20230316
001461471 1112_ $$aACM Conference on Recommender Systems$$n(4th :$$d2022)
001461471 24510 $$aRecommender systems in fashion and retail :$$bproceedings of the fourth Workshop at the Recommender Systems Conference (2022) /$$cedited by Humberto Jesús Corona Pampín, Reza Shirvany.
001461471 264_1 $$aCham :$$bSpringer,$$c2023.
001461471 300__ $$a1 online resource (121 pages) :$$billustrations (colour).
001461471 336__ $$atext$$btxt$$2rdacontent
001461471 337__ $$acomputer$$bc$$2rdamedia
001461471 338__ $$aonline resource$$bcr$$2rdacarrier
001461471 4901_ $$aLecture notes in electrical engineering ;$$vvolume 981
001461471 500__ $$aConference proceedings.
001461471 5050_ $$a1. Identification of Fine-grained Fit Information from Customer Reviews in Fashion -- 2. Personalization through User Attributes for Transformer-based Sequential Recommendation -- 3. Reusable Self-Attention-based Recommender System for Fashion -- 4. Adversarial Attacks against Visually-aware Fashion Outfit Recommender Systems -- 5. Contrastive Learning for Topic-Dependent Image Ranking -- 6. A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail -- 7. End-to-End Image-Based Fashion Recommendation.
001461471 506__ $$aAccess limited to authorized users.
001461471 520__ $$aThis book includes the proceedings of the fourth workshop on recommender systems in fashion and retail (2022), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering 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 product recommendations, size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).
001461471 588__ $$aDescription based on print version record.
001461471 650_0 $$aRecommender systems (Information filtering)$$vCongresses.
001461471 650_0 $$aElectronic commerce$$xAutomation$$vCongresses.
001461471 650_0 $$aElectronic commerce$$xStatistical methods$$vCongresses.
001461471 650_0 $$aRetail trade$$xAutomation$$vCongresses.
001461471 655_0 $$aElectronic books.
001461471 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001461471 7001_ $$aPampín, Humberto Jesús Corona,$$eeditor.
001461471 7001_ $$aShirvany, Reza,$$eeditor.
001461471 77608 $$iPrint version:$$aACM Conference on Recommender Systems (4th : 2022), creator.$$tRecommender systems in fashion and retail.$$dCham : Springer Nature Switzerland, 2023$$z9783031221910$$w(OCoLC)1359607795
001461471 830_0 $$aLecture notes in electrical engineering ;$$vv. 981.
001461471 852__ $$bebk
001461471 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-22192-7$$zOnline Access$$91397441.1
001461471 909CO $$ooai:library.usi.edu:1461471$$pGLOBAL_SET
001461471 980__ $$aBIB
001461471 980__ $$aEBOOK
001461471 982__ $$aEbook
001461471 983__ $$aOnline
001461471 994__ $$a92$$bISE