Outfit Generation and Recommendation -- An Experimental Study

• URL: http://arxiv.org/abs/2211.16353v1
• Date: Tue, 29 Nov 2022 16:36:00 GMT
• Title: Outfit Generation and Recommendation -- An Experimental Study
• Authors: Marjan Celikik, Matthias Kirmse, Timo Denk, Pierre Gagliardi, Sahar Mbarek, Duy Pham, Ana Peleteiro Ramallo
• Abstract summary: We compare different algorithms for outfit generation and recommendation using online, real-world user data from one of Europe's largest fashion stores. We present the adaptations we made to some of those models to make them suitable for personalized outfit generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past years, fashion-related challenges have gained a lot of attention in the research community. Outfit generation and recommendation, i.e., the composition of a set of items of different types (e.g., tops, bottom, shoes, accessories) that go well together, are among the most challenging ones. That is because items have to be both compatible amongst each other and also personalized to match the taste of the customer. Recently there has been a plethora of work targeted at tackling these problems by adopting various techniques and algorithms from the machine learning literature. However, to date, there is no extensive comparison of the performance of the different algorithms for outfit generation and recommendation. In this paper, we close this gap by providing a broad evaluation and comparison of various algorithms, including both personalized and non-personalized approaches, using online, real-world user data from one of Europe's largest fashion stores. We present the adaptations we made to some of those models to make them suitable for personalized outfit generation. Moreover, we provide insights for models that have not yet been evaluated on this task, specifically, GPT, BERT and Seq-to-Seq LSTM.

Related papers

• Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard [47.73060223236792]
  BEIR is a benchmark dataset for evaluation of information retrieval models across 18 different domain/task combinations. Our work addresses two shortcomings that prevent the benchmark from achieving its full potential.
  arXiv  Detail & Related papers  (2023-06-13T00:26:18Z)
• Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters [66.17039929803933]
  We propose a novel transfer learning framework which updates only $0.3%$ of model parameters to learn style specific attributes for response generation. We learn style specific attributes from the PERSONALITY-CAPTIONS dataset.
  arXiv  Detail & Related papers  (2022-10-07T00:09:22Z)
• Recommendation of Compatible Outfits Conditioned on Style [22.03522251199042]
  This work aims to generate outfits conditional on styles or themes as one would dress in real life. We use a novel style encoder network that renders outfit styles in a smooth latent space.
  arXiv  Detail & Related papers  (2022-03-30T09:23:32Z)
• PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
  PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction. We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
  arXiv  Detail & Related papers  (2021-05-04T15:23:59Z)
• Learning Tuple Compatibility for Conditional OutfitRecommendation [13.265372545945112]
  Mixed Category Attention Net (MCAN) integrates fine-grained and coarse category information into recommendation. MCAN can explicitly and effectively generate diverse and controllable recommendations based on need. New dataset IQON can be used to test the generalization of recommendation systems.
  arXiv  Detail & Related papers  (2020-08-18T23:22:16Z)
• Apparel-invariant Feature Learning for Apparel-changed Person Re-identification [70.16040194572406]
  Most public ReID datasets are collected in a short time window in which persons' appearance rarely changes. In real-world applications such as in a shopping mall, the same person's clothing may change, and different persons may wearing similar clothes. It is critical to learn an apparel-invariant person representation under cases like cloth changing or several persons wearing similar clothes.
  arXiv  Detail & Related papers  (2020-08-14T03:49:14Z)
• Bootstrapping Complete The Look at Pinterest [8.503851753592512]
  We will describe how we bootstrapped the Complete The Look (CTL) system at Pinterest. This is a technology that aims to learn the subjective task of "style compatibility" in order to recommend complementary items that complete an outfit. We will introduce our outfit dataset of over 1 million outfits and 4 million objects, a subset of which we will make available to the research community.
  arXiv  Detail & Related papers  (2020-06-18T18:20:19Z)
• Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach [71.63618051547144]
  We study the problem of personalized fashion recommendation from social media data. We present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item. To validate the effectiveness of our approach, we collect a real-world social media dataset.
  arXiv  Detail & Related papers  (2020-05-25T23:24:24Z)
• Fashion Recommendation and Compatibility Prediction Using Relational Network [18.13692056232815]
  We develop a Relation Network (RN) to develop new compatibility learning models. FashionRN learns the compatibility of an entire outfit, with an arbitrary number of items, in an arbitrary order. We evaluate our model using a large dataset of 49,740 outfits that we collected from Polyvore website.
  arXiv  Detail & Related papers  (2020-05-13T21:00:54Z)
• Learning Diverse Fashion Collocation by Neural Graph Filtering [78.9188246136867]
  We propose a novel fashion collocation framework, Neural Graph Filtering, that models a flexible set of fashion items via a graph neural network. By applying symmetric operations on the edge vectors, this framework allows varying numbers of inputs/outputs and is invariant to their ordering. We evaluate the proposed approach on three popular benchmarks, the Polyvore dataset, the Polyvore-D dataset, and our reorganized Amazon Fashion dataset.
  arXiv  Detail & Related papers  (2020-03-11T16:17:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.