Contrastive language and vision learning of general fashion concepts.

The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from general and transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model adapted for the fashion industry.

Draw a Star and Make it Perfect: Incremental Processing of Telicity.

Predicates like "coloring-the-star" denote events that have a temporal duration and a culmination point (telos). When combined with perfective aspect (e.g.

Shopper intent prediction from clickstream e-commerce data with minimal browsing information.

We address the problem of user intent prediction from clickstream data of an e-commerce website via two conceptually different approaches: a hand-crafted feature-based classification and a deep learning-based classification. In both approaches, we deliberately coarse-grain a new clickstream proprietary dataset to produce symbolic trajectories with minimal information. Then, we tackle the problem of trajectory classification of arbitrary length and ultimately, early prediction of limited-length trajectories, both for balanced and unbalanced datasets.