Showing posts from Topic Model category
Cross-modal Search for Fashion Attributes
In this paper we develop a neural network which learns inter- modal representations for fashion attributes to be utilized in a cross-modal search tool. Our neural network learns from organic e-commerce data, which is characterized by clean image material, but noisy and incomplete product descrip- tions. First, we experiment with techniques to segment e- commerce images and their product descriptions into respec- tively image and text fragments denoting fashion attributes. Here, we propose a …
Latent Dirichlet Allocation for Linking User-Generated Content and e-Commerce Data
Automatic linking of online content improves navigation possibilities for end users. We focus on linking content generated by users to other relevant sites. In particular, we study the problem of linking information between different usages of the same language, e.g., colloquial and formal idioms or the language of consumers versus the language of sellers. The challenge is that the same items are described using very distinct vocabularies. As a case study, we investigate a new task of linking …
Learning to Bridge Colloquial and Formal Language Applied to Linking and Search of E-Commerce Data
We study the problem of linking information between different idiomatic usages of the same language, for example, colloquial and formal language. We propose a novel probabilistic topic model called multi-idiomatic LDA (MiLDA). Its modeling principles follow the intuition that certain words are shared between two idioms of the same language, while other words are non-shared. We demonstrate the ability of our model to learn relations between cross-idiomatic topics in a dataset containing product …
Are words enough?: a study on text-based representations and retrieval models for linking pins to online shops
User-generated content offers opportunities to learn about people’s interests and hobbies. We can leverage this infor- mation to help users find interesting shops and businesses find interested users. However this content is highly noisy and unstructured as posted on social media sites and blogs. In this work we evaluate different textual representations and retrieval models that aim to make sense of social media data for retail applications. Our task is to link the text of pins (from …