$A^3$-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit Preference Prediction

Abstract

With the booming development of the online fashion industry, effective personalized recommender systems have become indispensable for the convenience they brought to the customers and the profits to the e-commercial platforms. Estimating the user’s preference towards the outfit is at the core of a personalized recommendation system. Existing works on fashion recommendation are largely centering on modelling the clothing compatibility without considering the user factor or characterizing the user’s preference over the single item. However, how to effectively model the outfits with either few or even none interactions, is yet under-explored. In this paper, we address the task of personalized outfit preference prediction via a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttentive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttribute- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> ashion <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$A^3$</tex-math></inline-formula> -FKG), which is incorporated to build the association between different outfits with both outfit- and item- level attributes. Additionally, a two-level attention mechanism is developed to capture the user’s preference: 1) User-specific relation-aware attention layer, which captures the user’s fine-grained preferences with different focus on relations for learning outfit representation; 2) Target-aware attention layer, which characterizes the user’s latent diverse interests from his/her behavior sequences for learning user representation. Extensive experiments conducted on a large-scale fashion outfit dataset demonstrate significant improvements over other methods, which verify the excellence of our proposed framework.