  The popularity of online fashion shopping continues to grow. The ability to
offer an effective recommendation to customers is becoming increasingly
important. In this work, we focus on Fashion Outfits Challenge, part of SIGIR
2022 Workshop on eCommerce. The challenge is centered around Fill in the Blank
(FITB) task that implies predicting the missing outfit, given an incomplete
outfit and a list of candidates. In this paper, we focus on applying siamese
networks on the task. More specifically, we explore how combining information
from multiple modalities (textual and visual modality) impacts the performance
of the model on the task. We evaluate our model on the test split provided by
the challenge organizers and the test split with gold assignments that we
created during the development phase. We discover that using both visual, and
visual and textual data demonstrates promising results on the task. We conclude
by suggesting directions for further improvement of our method.
