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Deep cross-domain fashion recommendation
KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
2017 (engelsk)Inngår i: RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, Association for Computing Machinery (ACM), 2017, s. 407-410Konferansepaper (Fagfellevurdert)
Abstract [en]

With the increasing number of online shopping services, the number of users and the quantity of visual and textual information on the Internet, there is a pressing need for intelligent recommendation systems that analyze the user's behavior amongst multiple domains and help them to find the desirable information without the burden of search. However, there is little research that has been done on complex recommendation scenarios that involve knowledge transfer across multiple domains. This problem is especially challenging when the involved data sources are complex in terms of the limitations on the quantity and quality of data that can be crawled. The goal of this paper is studying the connection between visual and textual inputs for better analysis of a certain domain, and to examine the possibility of knowledge transfer from complex domains for the purpose of efficient recommendations. The methods employed to achieve this study include both design of architecture and algorithms using deep learning technologies to analyze the effect of deep pixel-wise semantic segmentation and text integration on the quality of recommendations. We plan to develop a practical testing environment in a fashion domain.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2017. s. 407-410
Emneord [en]
CNN, Cross-domain knowledge transfer, Deep learning, Domain adaptation, Fashion recommendation, Transfer learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-218546DOI: 10.1145/3109859.3109861Scopus ID: 2-s2.0-85030469064ISBN: 9781450346528 (tryckt)OAI: oai:DiVA.org:kth-218546DiVA, id: diva2:1161447
Konferanse
11th ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, 27 August 2017 through 31 August 2017
Merknad

QC 20171130

Tilgjengelig fra: 2017-11-30 Laget: 2017-11-30 Sist oppdatert: 2019-10-17bibliografisk kontrollert

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