Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap

• URL: http://arxiv.org/abs/2501.14826v1
• Date: Tue, 21 Jan 2025 23:47:39 GMT
• Title: Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap
• Authors: Srivatsa Mallapragada, Ying Xie, Varsha Rani Chawan, Zeyad Hailat, Yuanbo Wang,
• Abstract summary: PINCER transforms initial user queries into pseudo-product representations. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval.
• Score: 1.2356255208135267
• License:
• Abstract: E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data sources, our model can infer users' potential purchase intent from their limited queries and capture query relevant product features. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval in both controlled and real-world experiments. Our ablation studies confirm that the proposed transformer architecture and integrated learning strategies enable the mining of key data sources to infer purchase intent, extract product features, and enhance the transformation pipeline from queries to more accurate pseudo-product representations.

Related papers

• Multimodal semantic retrieval for product search [6.185573921868495]
  We build a multimodal representation for product items in e-commerce search in contrast to pure-text representation of products. We demonstrate that a multimodal representation scheme for a product can show improvement on purchase recall or relevance accuracy in semantic retrieval.
  arXiv  Detail & Related papers  (2025-01-13T14:34:26Z)
• MMGRec: Multimodal Generative Recommendation with Transformer Model [81.61896141495144]
  MMGRec aims to introduce a generative paradigm into multimodal recommendation. We first devise a hierarchical quantization method Graph CF-RQVAE to assign Rec-ID for each item from its multimodal information. We then train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences.
  arXiv  Detail & Related papers  (2024-04-25T12:11:27Z)
• Enhanced E-Commerce Attribute Extraction: Innovating with Decorative Relation Correction and LLAMA 2.0-Based Annotation [4.81846973621209]
  We propose a pioneering framework that integrates BERT for classification, a Conditional Random Fields (CRFs) layer for attribute value extraction, and Large Language Models (LLMs) for data annotation. Our approach capitalizes on the robust representation learning of BERT, synergized with the sequence decoding prowess of CRFs, to adeptly identify and extract attribute values. Our methodology is rigorously validated on various datasets, including Walmart, BestBuy's e-commerce NER dataset, and the CoNLL dataset.
  arXiv  Detail & Related papers  (2023-12-09T08:26:30Z)
• Zero-shot Composed Text-Image Retrieval [72.43790281036584]
  We consider the problem of composed image retrieval (CIR) It aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability.
  arXiv  Detail & Related papers  (2023-06-12T17:56:01Z)
• Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data [66.21317300595483]
  We show how recent advances in machine learning, combined with a recently published multilingual data set, enable robust product attribute extraction. Our models can reliably predict product attributes across online shops, languages, or both.
  arXiv  Detail & Related papers  (2023-02-23T16:26:11Z)
• A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data [7.427088261927881]
  The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.
  arXiv  Detail & Related papers  (2022-11-04T15:57:19Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• 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)
• 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)
• Probing Product Description Generation via Posterior Distillation [39.65544829475079]
  High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. Existing works tend to generate the product description solely based on item information. We propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews.
  arXiv  Detail & Related papers  (2021-03-02T09:38:38Z)
• Personalized Embedding-based e-Commerce Recommendations at eBay [3.1236273633321416]
  We present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in the same vector space. Data ablation is incorporated into the offline model training process to improve the robustness of the production system.
  arXiv  Detail & Related papers  (2021-02-11T17:58:51Z)

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.