Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling

• URL: http://arxiv.org/abs/2008.01437v1
• Date: Tue, 4 Aug 2020 10:07:09 GMT
• Title: Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling
• Authors: Dhruv Verma, Kshitij Gulati and Rajiv Ratn Shah
• Abstract summary: This paper attempts to address the cold-start problem for new users by leveraging a novel visual preference modelling approach. We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation.
• Score: 51.147871738838305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the global transformation of the fashion industry and a rise in the demand for fashion items worldwide, the need for an effectual fashion recommendation has never been more. Despite various cutting-edge solutions proposed in the past for personalising fashion recommendation, the technology is still limited by its poor performance on new entities, i.e. the cold-start problem. In this paper, we attempt to address the cold-start problem for new users, by leveraging a novel visual preference modelling approach on a small set of input images. We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation. Quantitatively, our results show that the proposed visual preference modelling approach outperforms state of the art in terms of clothing attribute prediction. Qualitatively, through a pilot study, we demonstrate the efficacy of our system to provide diverse and personalised recommendations in cold-start scenarios.

Related papers

• Interactive Garment Recommendation with User in the Loop [77.35411131350833]
  We propose to build a user profile on the fly by integrating user reactions as we recommend complementary items to compose an outfit. We present a reinforcement learning agent capable of suggesting appropriate garments and ingesting user feedback to improve its recommendations.
  arXiv  Detail & Related papers  (2024-02-18T16:01:28Z)
• HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models [84.12784265734238]
  The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. We propose HiCAST, which is capable of explicitly customizing the stylization results according to various source of semantic clues. A novel learning objective is leveraged for video diffusion model training, which significantly improve cross-frame temporal consistency.
  arXiv  Detail & Related papers  (2024-01-11T12:26:23Z)
• AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method [2.2975420753582028]
  This research describes an innovative end-to-end pipeline that uses artificial intelligence to provide fine-grained visual interpretation for fashion recommendations. When customers upload images of desired products or outfits, the system automatically generates meaningful descriptions emphasizing stylistic elements.
  arXiv  Detail & Related papers  (2023-11-16T07:15:44Z)
• Lost Your Style? Navigating with Semantic-Level Approach for Text-to-Outfit Retrieval [2.07180164747172]
  We introduce a groundbreaking approach to fashion recommendations: text-to-outfit retrieval task that generates a complete outfit set based solely on textual descriptions. Our model is devised at three semantic levels-item, style, and outfit-where each level progressively aggregates data to form a coherent outfit recommendation. Using the Maryland Polyvore and Polyvore Outfit datasets, our approach significantly outperformed state-of-the-art models in text-video retrieval tasks.
  arXiv  Detail & Related papers  (2023-11-03T07:23:21Z)
• Reusable Self-Attention-based Recommender System for Fashion [1.978884131103313]
  We present a reusable Attention-based Fashion Recommendation Algorithm (AFRA) We leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item recommendation and 4) in-session recommendations inspired by most recent customer actions.
  arXiv  Detail & Related papers  (2022-11-29T16:47:20Z)
• Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media [72.06420633156479]
  We propose an improved model named Relation Enhanced Attention Recurrent (REAR) network. Compared to KERN, the REAR model leverages not only the relations among fashion elements but also those among user groups. To further improve the performance of long-range trend forecasting, the REAR method devises a sliding temporal attention mechanism.
  arXiv  Detail & Related papers  (2021-05-07T14:52:03Z)
• 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)
• 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.