The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or specific query styles but do not consider customer interests in famous characters from movie, social media, etc. In this paper, we define a new Character-based Outfit Generation (COG) problem, designed to accurately interpret character information and generate complete outfit sets according to customer specifications such as age and gender. To tackle this problem, we propose a novel framework LVA-COG that leverages Large Language Models (LLMs) to extract insights from customer interests (e.g., character information) and employ prompt engineering techniques for accurate understanding of customer preferences. Additionally, we incorporate text-to-image models to enhance the visual understanding and generation (factual or counterfactual) of cohesive outfits. Our framework integrates LLMs with text-to-image models and improves the customer’s approach to fashion by generating personalized recommendations. With experiments and case studies, we demonstrate the effectiveness of our solution from multiple dimensions.

In e-Commerce Industry, customers’ purchase decision of an item are usually influenced by the reference price of that item, which is implied within the context of the items (e.g. prices of an item set from search/recommendation) or external environments (e.g. prices from another e-Commerce platform). Despite of the prevalence and influence of the reference price on customers’ behavior, existing works in Information Retrieval domain do not exploit the value of the reference price in ranking problems. In this paper, we propose a list-wise ranking model named "Prospect-Net" by incorporating the prospect theory, which is the theoretical foundation for framing the reference price. We consider the Top-K retrieval task under a product recommendation setting, and demonstrate the effectiveness of Prospect-Net to capture various forms of reference price under different scenarios. Polynomial solutions are proposed to solve the Top-K retrieval problem for some of the cases where the reference price is dependent on the recommended set of items to the user. Both offline e valuation and online experiments are performed on a real-world industrial dataset with significant performance improvement.