Modeling instant user intent and content‑level transition for sequential fashion recommendation

Publication Type

Journal Article

Publication Date

6-2021

Abstract

Fashion recommendation, aiming to explore specific user preference in fashion, has become an important research topic for its practical significance to the fashion business sector. However, little work has been done on an important sub-task called sequential fashion recommendation, which aims to capture additional short-term fashion interest of users by modeling the item-to-item transitions. In this paper, we propose a novel Attentional Content-level Translation-based Recommender (ACTR) framework, which simultaneously models the instant user intent of each transition and the intent-specific transition probability. Specifically, we define instant intent with the relationships between adjacent items that the users interacted, which are the three fundamental domain-specific relationships of: match, substitute and others. To further exploit the characteristics of fashion domain and alleviate the item transition sparsity problem, we augment the item-level transition modeling with multiple sub-transitions using various content-level attributes. An attention mechanism is further devised to effectively aggregate multiple content-level transitions. To the best of our knowledge, this is the first work that specifies the implicit user actions in online fashion shopping with explicit instant intent, which enhances the connectivity of fashion items and boosts the recommendation performance. Extensive experiments on two real-world fashion E-commerce datasets demonstrate the effectiveness of the proposed method in sequential fashion recommendation.

Keywords

Fashion recommendation, Instant intent modeling, Translation method

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Multimedia

Volume

24

First Page

2687

Last Page

2700

ISSN

1520-9210

Identifier

10.1109/TMM.2021.3088281

Publisher

Institute of Electrical and Electronics Engineers

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