Search Results for « Fashion Knowledge Graph »

The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KGs) offer a structured, interpretable format to organize such data, yet constructing product-specific KGs remains a complex and manual process. This paper introduces a fully automated, AI agent-driven framework for constructing product knowledge graphs directly from unstructured product descriptions. Leveraging Large Language Models (LLMs), our method operates in three stages using dedicated agents: ontology creation and expansion, ontology refinement, and knowledge graph population. This agent-based approach ensures semantic coherence, scalability, and high-quality output without relying on predefined schemas or handcrafted extraction rules. We evaluate the system on a real-world dataset of air conditioner product descriptions, demonstrating strong performance in both ontology generation and KG population. The framework achieves over 97\% property coverage and minimal redundancy, validating its effectiveness and practical applicability. Our work highlights the potential of LLMs to automate structured knowledge extraction in retail, providing a scalable path toward intelligent product data integration and utilization.

Knowledge Graphs offer a very useful and powerful structure for representing information, consequently, they have been adopted as the backbone for many applications in e-commerce scenarios. In this paper, we describe an application of existing techniques for enriching thelarge-scale Fashion Knowledge Graph (FKG) that we build at Farfetch. In particular, we apply techniques for named entity recognition (NER) and entity linking (EL) in order to extract and link rich metadata from product textual descriptions to entities in the FKG. Having a complete and enriched FKG as an e-commerce backbone can have a highly valuable impact on downstream applications such as search and recommendations. However, enriching a Knowledge Graph in the fashion domain has its own challenges. Data representation is different from a more generic KG, like Wikidata and Yago, as entities (e.g. product attributes) are too specific to the domain, and long textual descriptions are not readily available. Data itself is also scarce, as labelling datasets to train supervised models is a very laborious task. Even more, fashion products display a high variability and require an intricate ontology of attributes to link to. We use a transfer learning based approach to train an NER module on a small amount of manually labeled data, followed by an EL module that links the previously identified named entities to the appropriate entities within the FKG. Experiments using a pre-trained model show that it is possible to achieve 89.75% accuracy in NER even with a small manually labeled dataset. Moreover, the EL module, despite relying on simple rule-based or ML models (due to lack of training data), is able to link relevant attributes to products, thus automatically enriching the FKG.

E-commerce recommender systems (RSs) can help users quickly find what they need or new products they might be interested in. To continuously enhance user trust in the website, improve page visits and dwell time, and most importantly, increase gross merchandise value (GMV), it is crucial to understand and capture the important information hidden in the data, which has a great impact on user choice. The fashion e-commerce websites can collect the attributes of items and users as well as the user purchase behaviors, but lack the fine-grained classification of the items and the implicit relationship between items and users. This paper focuses on Amazon fashion dataset, one of the most widely used datasets in the fashion field. A differentiated recommendation framework is proposed that provides different recommendation paths for active and inactive users to improve the overall recommendation quality. In the framework, a data augmentation algorithm based on transfer learning is proposed to filter out the irrelevant items and label items with fine-grained tags, and a user-item knowledge graph is built to discover the potential relationship between items and users. Finally, a differentiated recommendation strategy is put forward to make different recommendations for users with different characteristics. The experimental results show that through data augmentation algorithm to improve data quality, factorization machine model produces higher recommendation accuracy, the constructed knowledge graph can alleviate the cold start problem for recommendation, and the differentiated recommendation strategy has achieved better recommendations for both active and inactive users.

Most knowledge graph use cases are data-centric, focusing on representing data entities and their semantic relationships. There are no published success stories to represent large-scale complicated business logic with knowledge graph technologies. In this paper, we will share our innovative and practical approach to representing complicated U.S. and Canadian income tax compliance logic (calculations and rules) via a large-scale knowledge graph. We will cover how the Tax Knowledge Graph is constructed and automated, how it is used to calculate tax refunds, reasoned to find missing info, and navigated to explain the calculated results. The Tax Knowledge Graph has helped transform Intuit's flagship TurboTax product into a smart and personalized experience, accelerating and automating the tax preparation process while instilling confidence for millions of customers.

Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large Language Models (LLMs) have transformed many areas, their application in fashion remains limited by hallucinations and weak domain specialization. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) offers a promising way to add structured knowledge to LLMs. However, existing fashion KGs are typically restricted to product-level attributes or item relations, and fail to capture the broader fashion ecosystem. To bridge these gaps, we propose \textbf{FashionEcoKG}, a comprehensive, domain-wide knowledge graph built with expert-level precision and professionalism. It is constructed through a three-stage agentic pipeline that extracts high-fidelity knowledge cores from authoritative textbooks and strengthens structural connectivity through cross-domain augmentation and generative expansion. To leverage this resource, we further develop \textbf{PG-RAG} (Pruning-Grounding RAG), a training-free framework designed to handle the conceptual density and linguistic noise of fashion queries. Specifically, we introduce a Dual-Granularity Path Re-Ranking (DGPR) module of two stages. The Pruning-based Semantic Ranking (PSR) module distills each query into a skeleton form to improve retrieval recall, while the Grounding-based Agentic Ranking (GAR) performs point-wise scrutiny of candidate paths against the original full query to ensure global relevance. Experiments on a curated fashion QA dataset show that PG-RAG effectively leverages FashionEcoKG to improve retrieval and answer accuracy, outperforming both non-RAG and existing KG-RAG baselines.

With the continuous advancement of artificial intelligence technology, knowledge graphs and large language models have shown great potential in the communication strategies of fashion brands. This paper presents innovative approaches that combine the two, aiming to elevate fashion brands' narrative generation and personalized communication strategies. By building a model based on the combination of knowledge graph and LLM, fashion brands can better understand consumer needs and generate more accurate brand stories, thereby improving market responsiveness and brand loyalty. The experimental results show that this combination method shows significant advantages in multiple dimensions. By analyzing the user interaction data of different social platforms, by analyzing the user interaction data of different social platforms, it is found that the TikTok platform has the largest amount of user interaction (reaching 150,000 total interactions, including 100,000 likes), showing a high degree of engagement of the platform. Other platforms like Instagram and Weibo have lower user interaction, showing a high degree of engagement of the platform. Other platforms like Instagram and Weibo have lower user interaction, especially in terms of the number of comments and shares. According to this data, fashion brands can pay more attention to interactivity with target consumers when choosing communication platforms. In the research of personalized communication strategy, through big data and artificial intelligence technology, fashion brands can accurately push content and make real-time adjustments according to consumers' interests and behaviors. Experiments show that personalized recommendation content can increase consumers' purchase intention to 30%, and improve consumers' satisfaction and repurchase intention. The quality analysis of narrative generation based on LLM shows that with the improvement of automated evaluation scores, the scores of multiple dimensions such as narrative, fluency, and coherence gradually increase, showing the great potential of large language models in fashion brand narrative generation. Combining the structured information of knowledge graphs with the text generation capabilities of LLM, brands can ensure the personalization and accuracy of communication content while maintaining creativity.

A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep-learning recommenders live up to their full potential only when numerous features of heterogeneous types are included. Also, typically recommendation models are designed to serve well only a single use case, which increases modeling complexity and maintenance costs, and may lead to inconsistent customer experience. In this work, we present a reusable Attention-based Fashion Recommendation Algorithm (AFRA), that utilizes various interaction types with different fashion entities such as items (e.g., shirt), outfits and influencers, and their heterogeneous features. Moreover, 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. We present both offline and online experimental results demonstrating substantial improvements in customer retention and engagement.