Diversity in Fashion Recommendation Using Semantic Parsing

Abstract

Developing recommendation system for fashion images is challenging due to the inherent ambiguity associated with what criterion a user is looking at. Suggesting multiple images where each output image is similar to the query image on the basis of a different feature or part is one way to mitigate the problem. Existing works for fashion recommendation have used Siamese or Triplet network to learn features between a similar pair and a similar-dissimilar triplet respectively. However, these methods do not provide basic information such as, how two clothing images are similar, or which parts present in the two images make them similar. In this paper, we propose to recommend images by explicitly learning and exploiting part based similarity. We propose a novel approach of learning discriminative features from weakly-supervised data by using visual attention over the parts and a texture encoding network. We show that the learned features surpass the state-of-the-art in retrieval task on DeepFashion dataset. We then use the proposed model to recommend fashion images having an explicit variation with respect to similarity of any of the parts.

Fashion Recommendation Systems, Models and Methods: A Review

“…Various research projects on apparel recommendation systems with personalized styling guideline and intelligent recommendation engines have been conducted based on similarity recommendation and expert advisor recommendation systems [10,58,61]. Image processing, image parsing, sensory engineering, computational algorithms, and computer vision techniques have been extensively employed to support these systems [32,[80][81][82][83][84].…”

Learning Context-Aware Outfit Recommendation

“…Fashion outfit is formed by people's random collocation of clothes and is strongly subjective [8], which makes it difficult to determine with a calculation model. If the style is identified by low-dimensional visual similarity [2,9], it may be too specific to detect similar-style images, and the hand-made style may be too abstract to capture subtle style differences. The clothing style is different from clothing shape (sweater, dress) or clothing attribute [10][11][12].…”