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[null,"1.","Record Nr.",null,"UNISA996391869203316"]
[null,null,"Autore",null,"Broughton Richard"]
[null,null,"Titolo",null,"Monastichon Britanicum [[electronic resource] ] : or, a historicall narration of the first founding and flourishing state of the antient monasteries, religious rules and orders of Great Brittaine, in the tymes of the Brittaines and primitive church of the Saxons. Collected out of the most authentick authors, lieger books, and manuscripts. By that learned antiquary, R.B"]
[null,null,"Pubbl/distr/stampa",null,"London, : Printed for Henry Herringman at the Anchor in the New Exchange, 1655"]
[null,null,"Descrizione fisica",null,"[16], 411, [5] p"]
[null,null,"Soggetti",null,"Monasticism and religious orders - Great Britain"]
[{"$nulls":4},"Great Britain Church history To 449"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Preface signed: Richard Broughton."]
[{"$nulls":4},"A reissue, with cancel title page, of: A true memorial of the ancient, most holy, and religious state of Great Britain."]
[{"$nulls":4},"Annotation on Thomason copy: \"Febr. 23.\"; the second 5 in the imprint date has been crossed out and replaced with \"4\"."]
[{"$nulls":4},"Reproduction of the original in the British Library."]
[null,null,null,"Sommario/riassunto","eebo-0018"]
[null,"2.","Record Nr.",null,"UNINA9910484596403321"]
[null,null,"Titolo",null,"Fashion recommender systems / / edited by Nima Dokoohaki"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2020]"]
[{"$nulls":4},"Â©2020"]
[null,null,"ISBN",null,"3-030-55218-7"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (VII, 145 p. 64 illus., 44 illus. in color.)"]
[null,null,"Collana",null,"Lecture Notes in Social Networks, , 2190-5428"]
[null,null,"Disciplina",null,"004.019"]
[null,null,"Soggetti",null,"Fashion merchandising - Data processing"]
[{"$nulls":4},"Human-computer interaction"]
[{"$nulls":4},"Recommender systems (Information filtering)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Part 1. Cold Start in Recommendations -- Chapter 1. Fashion Recommender Systems in Cold Start ( Mehdi Elahi) -- Part 2. Complementary and Session Based Recommendation -- Chapter 2. Enabling Hyper-Personalisation: Automated AdCreative Generation and Ranking for Fashion e-Commerce (Sreekanth Vempati) -- Chapter 3. Two-Stage Session-based Recommendations with Candidate Rank Embeddings (Jose Antonio Sanchez Rodrguez) -- Part 3. Outfit Recommendations -- Chapter 4. Attention-based Fusion for Outfit Recommendation (Katrien Laenen) -- Chapter 5. Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits’ Recommendation (Shatha Jaradat) -- Part 4. Sizing and Fit Recommendations -- Chapter 6. Learning Size and Fit from Fashion Images (Nour Karessli) -- Part 5. Generative Outfit Recommendation -- Chapter 7. Generating High-Resolution Fashion Model Images Wearing Custom Outfits (Gokhan Yildirim)."]
[null,null,null,"Sommario/riassunto","This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit "]
[{"$nulls":4},"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."]