Visually-Aware Fashion Recommendation and Design with Generative Image Models

By Wang-Cheng Kang, Chen Fang, Zhaowen Wang, Julian McAuley. Industrial Conference on Data Mining, 2017

298 citations recorded by the source corpus.

Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fashion styles). Recent work has shown that approaches to 'visual' recommendation (e.g. clothing, art, etc.) can be made more accurate by incorporating visual signals directly into the recommendation objective, using 'off-the-shelf' feature representations derived from deep networks. Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning 'fashion aware' image representations directly, i.e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pretrained visual features. Furthermore, we show that our model can be used generatively, i.e., given a user and a product category, we can generate new images (i.e., clothing items) that are most consistent with their personal taste. This represents a first step towards building systems that go beyond recommending existing items from a product corpus, but which can be used to suggest styles and aid the design of new products.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/76d8ccc5a35affb08271fee720a24418c8943b44

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Qiang Liu
2. Shu Wu
3. Liang Wang
4. Wenwu Ou
5. Fei Sun
6. Yujie Lin
7. Maarten de Rijke
8. Zhumin Chen

Citations in the corpus

• Mining Latent Structures for Multimedia Recommendation (2021, methodology): “DVBPR [17] attempted to jointly train the image representation as well as the parameters in a recommender model.”
• Self-Attentive Sequential Recommendation (2018, unknown): “images [10], [11], text [12], [13], etc.”
• Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation (2018, methodology): “[11] use CNNs to learn image representations and jointly employ collaborating filtering to recommend fashion items for users.”
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (2019, background): “, text [23, 53], images [21, 55], and acoustic features [51] into CF models.”