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| Content Provider | ACM Digital Library |
|---|---|
| Author | Shivakumar, Sunaya Karahalios, Karrie Kumar, Ranjitha Vaccaro, Kristen Ding, Ziqiao |
| Abstract | The outfits people wear contain latent fashion concepts capturing styles, seasons, events, and environments. Fashion theorists have proposed that these concepts are shaped by design elements such as color, material, and silhouette. A dress may be "bohemian" because of its pattern, material, trim, or some combination of them: it is not always clear how low-level elements translate to high-level styles. In this paper, we use polylingual topic modeling to learn latent fashion concepts jointly in two languages capturing these elements and styles. Using this latent topic formation we can translate between these two languages through topic space, exposing the elements of fashion style. We train the polylingual topic model (PLTM) on a set of more than half a million outfits collected from Polyvore, a popular fashion-based social net- work. We present novel, data-driven fashion applications that allow users to express their needs in natural language just as they would to a real stylist and produce tailored item recommendations for these style needs. |
| Starting Page | 777 |
| Ending Page | 785 |
| Page Count | 9 |
| File Format | PDF MP4 |
| ISBN | 9781450341899 |
| DOI | 10.1145/2984511.2984573 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-16 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Polylingual topic modeling Fashion Elements Styles |
| Content Type | Audio Text |
| Resource Type | Article |
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