POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

Abstract

Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashion out t recommendation: the Compatibility of the generated fashion out ts, and the Personalization in the recommendation process. In this paper, we demonstrate these two requirements can be satis ed via building a bridge between out t generation and recommendation. rough large data analysis, we observe that people have similar tastes in individual items and out ts. erefore, we propose a Personalized Out t Generation (POG) model, which connects user preferences regarding individual items and out ts with Transformer architecture. Extensive o ine and online experiments provide strong quantitative evidence that our method outperforms alternative methods regarding both compatibility and personalization metrics. Furthermore, we deploy POG on a platform named Dida in Alibaba to generate personalized out ts for the users of the online application iFashion.is work represents a rst step towards an industrial-scale fashion out t generation and recommendation solution, which goes beyond generating out ts based on explicit queries, or merely recommending from existing out t pools. As part of this work, we release a large-scale dataset consisting of 1.01 million out ts with rich context information, and 0.28 billion user click actions from 3.57 million users. To the best of our knowledge, this dataset is the largest, publicly available, fashion related dataset, and the rst to provide user behaviors relating to both out ts and fashion items.

Learning Intents behind Interactions with Knowledge Graph for Recommendation

“…(1) We use the Amazon-Book and Last-FM datasets released by KGAT [41]; And (2) we further introduce the Alibaba-iFashion dataset [8] to investigate the effectiveness of item knowledge. This is a fashion outfit dataset collected from Alibaba online shopping systems.…”

Behavior sequence transformer for e-commerce recommendation in Alibaba

“…During the last decade, Recommender Systems (RSs) have been the most popular application in industry, and in the past five years, deep learning based methods have been widely used in industrial RSs, e.g., Google [2,3] and Airbnb [5]. In Alibaba, the largest ecommerce platform in China, RSs have been the key engine for its Gross Merchandise Volume (GMV) and revenues, and various deep learning based recommendation methods have been deployed in rich e-commerce scenarios [1,8,10,11,14,15,17,18]. As introduced in [15], the RSs in Alibaba are a two-stage pipeline: match and rank.…”

Bootstrapping Complete The Look at Pinterest

“…Here we present a modified approach that handles product-to-product recommendation. Many recommendation systems, such as Alibaba's iFashion [4], use past user engagement to train complementary models [21] [9]. We attempt the challenging task of bootstrapping such a system without explicit user data.…”