AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity

• URL: http://arxiv.org/abs/2411.14737v1
• Date: Fri, 22 Nov 2024 05:11:51 GMT
• Title: AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity
• Authors: Xiaomin Li, Junyi Sha,
• Abstract summary: We develop a forecasting model, the Fashion Demand Predictor, which integrates Transformer-based models and Random Forest to predict market popularity based on product images. We validate these results through surveys that gather human rankings of preferences. We show that products enhanced with "good" features show marked improvements in predicted popularity over their modified counterparts.
• Score: 1.3965477771846408
• License:
• Abstract: Identifying key product features that influence consumer preferences is essential in the fashion industry. In this study, we introduce a robust methodology to ascertain the most impactful features in fashion product images, utilizing past market sales data. First, we propose the metric called "influence score" to quantitatively assess the importance of product features. Then we develop a forecasting model, the Fashion Demand Predictor (FDP), which integrates Transformer-based models and Random Forest to predict market popularity based on product images. We employ image-editing diffusion models to modify these images and perform an ablation study, which validates the impact of the highest and lowest-scoring features on the model's popularity predictions. Additionally, we further validate these results through surveys that gather human rankings of preferences, confirming the accuracy of the FDP model's predictions and the efficacy of our method in identifying influential features. Notably, products enhanced with "good" features show marked improvements in predicted popularity over their modified counterparts. Our approach develops a fully automated and systematic framework for fashion image analysis that provides valuable guidance for downstream tasks such as fashion product design and marketing strategy development.

Related papers

• An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model [0.0]
  This paper proposes an improved CTR prediction model based on the xDeepFM architecture. By integrating a multi-head attention mechanism, the model can simultaneously focus on different aspects of feature interactions. Experimental results on the Criteo dataset demonstrate that the proposed model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-21T03:21:29Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges by developing an textitinfluence functions framework.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• Context-aware Advertisement Modeling and Applications in Rapid Transit Systems [1.342834401139078]
  We present an advertisement model using behavioral and tracking analysis. We present a model using the agent-based modeling (ABM) technique, with the target audience of rapid transit system users to target the right person for advertisement applications.
  arXiv  Detail & Related papers  (2024-09-16T02:59:36Z)
• Data Augmentation via Latent Diffusion for Saliency Prediction [67.88936624546076]
  Saliency prediction models are constrained by the limited diversity and quantity of labeled data. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
  arXiv  Detail & Related papers  (2024-09-11T14:36:24Z)
• Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems [12.913561033531249]
  This paper develops a re-ranking approach in dynamic settings with fair-exposure optimization driven by strategic agents. Joint objectives ensure the performance of recommendations while enhancing the visibility of tail items. We have substantiated the effectiveness and dominance of the proposed method especially on unveiling the potential of tail items.
  arXiv  Detail & Related papers  (2024-06-25T11:41:50Z)
• Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models [85.96013373385057]
  Fine-tuning text-to-image models with reward functions trained on human feedback data has proven effective for aligning model behavior with human intent. However, excessive optimization with such reward models, which serve as mere proxy objectives, can compromise the performance of fine-tuned models. We propose TextNorm, a method that enhances alignment based on a measure of reward model confidence estimated across a set of semantically contrastive text prompts.
  arXiv  Detail & Related papers  (2024-04-02T11:40:38Z)
• In the Eye of the Beholder: Robust Prediction with Causal User Modeling [27.294341513692164]
  We propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment.
  arXiv  Detail & Related papers  (2022-06-01T11:33:57Z)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• What Image Features Boost Housing Market Predictions? [81.32205133298254]
  We propose a set of techniques for the extraction of visual features for efficient numerical inclusion in predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors.
  arXiv  Detail & Related papers  (2021-07-15T06:32:10Z)
• 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)
• Interpretable Multiple Treatment Revenue Uplift Modeling [4.9571232160914365]
  Uplift models support a firm's decision-making by predicting the change of a customer's behavior due to a treatment. The paper extends corresponding approaches by developing uplift models for multiple treatments and continuous outcomes.
  arXiv  Detail & Related papers  (2021-01-09T11:29:00Z)

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.