01417nam2 22003373i 450 VAN0030104820251205113548.41888-368-0490-XIT98 729920251205d1997 |0itac50 baitaIT|||| |||||i e nnc1*scritti di Abagnale ... [et al.]FirenzePassigli1997359 p.21 cmFondo FerraraIT-IT-CE0105 CONSF.G.1878Fondo FerraraIT-IT-CE0105 CONSF.G.1878/bis001VAN003010472001 ˆLe ‰autorità indipendenti nei sistemi istituzionali ed economicia cura di Alberto Predieri210 FirenzePassigli215 1 volume21 cm300 Relazioni presentate a un convegno tenuto a Firenze nel 19961FirenzeVANL000014AbagnaleGloriaVANV255264Passigli <editore>VANV109283650ITSOL20251212RICAVAN00301048BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00CONS F.G.1878 00FG 1878 20251205 Fondo FerraraBIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00CONS F.G.1878 00FG 301048 bis 20251205 Fondo Ferrara161339UNICAMPANIA05424nam 22006255 450 991086318210332120251230065345.03-030-55218-710.1007/978-3-030-55218-3(CKB)4100000011558647(DE-He213)978-3-030-55218-3(MiAaPQ)EBC6384935(PPN)252508289(MiAaPQ)EBC29092905(EXLCZ)99410000001155864720201104d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierFashion Recommender Systems /edited by Nima Dokoohaki1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (VII, 145 p. 64 illus., 44 illus. in color.) Lecture Notes in Social Networks,2190-54363-030-55217-9 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 preferencestowards 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.Lecture Notes in Social Networks,2190-5436Graph theoryData miningSociologyMethodologySignal processingGraph TheoryData Mining and Knowledge DiscoverySociological MethodsSignal, Speech and Image ProcessingGraph theory.Data mining.SociologyMethodology.Signal processing.Graph Theory.Data Mining and Knowledge Discovery.Sociological Methods.Signal, Speech and Image Processing.004.019Dokoohaki NimaMiAaPQMiAaPQMiAaPQBOOK9910863182103321Fashion recommender systems2547739UNINA