Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce

• URL: http://arxiv.org/abs/2208.06261v1
• Date: Thu, 11 Aug 2022 16:47:25 GMT
• Title: Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce
• Authors: Oishik Chatterjee, Jaidam Ram Tej, Narendra Varma Dasaraju
• Abstract summary: We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions. Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over the baseline across the 4 different datasets.
• Score: 2.8360662552057323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the huge growth in e-commerce domain, product recommendations have become an increasing field of interest amongst e-commerce companies. One of the more difficult tasks in product recommendations is size and fit predictions. There are a lot of size related returns and refunds in e-fashion domain which causes inconvenience to the customers as well as costs the company. Thus having a good size and fit recommendation system, which can predict the correct sizes for the customers will not only reduce size related returns and refunds but also improve customer experience. Early works in this field used traditional machine learning approaches to estimate customer and product sizes from purchase history. These methods suffered from cold start problem due to huge sparsity in the customer-product data. More recently, people have used deep learning to address this problem by embedding customer and product features. But none of them incorporates valuable customer feedback present on product pages along with the customer and product features. We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions. We demonstrate the effectiveness of our approach compared to using just product and customer features on 4 datasets. Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over the baseline across the 4 different datasets.

Related papers

• A BERT based Ensemble Approach for Sentiment Classification of Customer Reviews and its Application to Nudge Marketing in e-Commerce [2.2120851074630177]
  Product reviews improve customer trust and loyalty. Nudge marketing is a subtle way for an ecommerce company to help their customers make better decisions without hesitation.
  arXiv  Detail & Related papers  (2023-11-16T14:18:24Z)
• Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation [127.35910314813854]
  We present the Amazon Multi-locale Shopping Session dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales. Remarkably, the dataset can help us enhance personalization and understanding of user preferences.
  arXiv  Detail & Related papers  (2023-07-19T00:08:49Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling [62.049330405736406]
  Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant. It is insufficient to provide the same "completely summarized" answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves. We propose a novel multi-perspective user preference model for generating personalized answers in PQA.
  arXiv  Detail & Related papers  (2021-12-27T07:51:49Z)
• Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity [54.26540039514418]
  This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
  arXiv  Detail & Related papers  (2021-11-11T05:36:39Z)
• Aggregated Customer Engagement Model [0.571097144710995]
  E-commerce websites use machine learned ranking models to serve shopping results to customers. New or under-impressed products do not have enough customer engagement signals and end up at a disadvantage when being ranked alongside popular products. We propose a novel method for data curation that aggregates all customer engagements within a day for the same query to use as input training data.
  arXiv  Detail & Related papers  (2021-08-17T20:58:10Z)
• SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce [3.324876873771105]
  We introduce SizeFlags, a probabilistic Bayesian model based on weakly annotated large-scale data from customers. We demonstrate the strong impact of the proposed approach in reducing size-related returns in online fashion over 14 countries.
  arXiv  Detail & Related papers  (2021-06-07T11:43:40Z)
• Style Similarity as Feedback for Product Design [7.241984306136333]
  We take a product design perspective, by employing big-data analysis for determining the design qualities of a highly recommended product. We build off previous work which implemented a style-based similarity metric for thousands of furniture products. We propose a designer in-the-loop workflow that mirrors methods of displaying similar products to consumers browsing e-commerce websites.
  arXiv  Detail & Related papers  (2021-05-25T23:30:29Z)
• 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)
• Deep Learning-based Online Alternative Product Recommendations at Scale [0.2278231643598956]
  We use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision.
  arXiv  Detail & Related papers  (2021-04-15T16:27:45Z)
• User-Inspired Posterior Network for Recommendation Reason Generation [53.035224183349385]
  Recommendation reason generation plays a vital role in attracting customers' attention as well as improving user experience. We propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests. Experimental results show that our model is superior to traditional generative models.
  arXiv  Detail & Related papers  (2021-02-16T02:08:52Z)

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.