POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

Chen, Wen; Huang, Pipei; Xu, Jiaming; Guo, Xin; Guo, Cheng; Sun, Fei; Li, Chao; Pfadler, Andreas; Zhao, Huan; Zhao, Binqiang

doi:10.48550/arxiv.1905.01866

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…We evaluate the performance of our proposed method on five public datasets: MovieLens-1M (ML-1M) [23], Yelp2018 [24], Amazon Books, Gowalla, and Alibaba-iFashion [1]. These datasets vary in domain, scale, and density.…”

Section: Datasetsmentioning

confidence: 99%

“…The advent of the digital age has led to an explosion of data, particularly in the realm of user-item interactions. This wealth of data has opened up new opportunities for recommendation systems [1], which aim to predict user preferences and recommend items that are most likely to be of interest. However, the sheer volume and complexity of the data present significant challenges.…”

Section: Introductionmentioning

confidence: 99%

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LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Cao,

Chen,

Hao

et al. 2023

Mathematics

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Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Cao,

Chen,

Hao

et al. 2023

Mathematics

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“…We evaluate our proposed model based on two real-world e-commerce datasets: iFashion [1] and Amazon review datasets [19].…”

Section: Datasetmentioning

confidence: 99%

“…Implementation details: We perform grid search on the embedding size of users and items, which is within [5,10,20,50] . In terms of the number of propagation layers of the u-i graph and i-i graph, we perform grid search within [1,2,3] for each type of graph respectively. The learning rate during training is set to 0.01 and the batch size is set to 5000 for all methods.…”

Section: Experimental Settingsmentioning

confidence: 99%

Leveraging Two Types of Global Graph for Sequential Fashion Recommendation

Ding

Wong

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. The key to building an effective sequential fashion recommendation model lies in capturing two types of patterns: the personal fashion preference of users and the transitional relationships between adjacent items. The two types of patterns are usually related to user-item interaction and item-item transition modeling respectively. However, due to the large sets of users and items as well as the sparse historical interactions, it is difficult to train an effective and efficient sequential fashion recommendation model. To tackle these problems, we propose to leverage two types of global graph, i.e., the user-item interaction graph and item-item transition graph, to obtain enhanced user and item representations by incorporating higher-order connections over the graphs. In addition, we adopt the graph kernel of LightGCN [10] for the information propagation in both graphs and propose a new design for item-item transition graph. Extensive experiments on two established sequential fashion recommendation datasets validate the effectiveness and efficiency of our approach. CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval.

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“…Many research efforts have been made in this domain, focusing on clothing recognition, 2-5 clothing retrieval, [6][7][8][9] clothing parsing, [10][11][12] clothing collocation, and recommendation. [13][14][15][16] We therefore focus on the research areas of clothing collocation. Clothing collocation measures whether a group of clothing items collocate with one another.…”

Section: Related Workmentioning

confidence: 99%

Clothing Color Collocation with Deep Neural Networks

Zhang

Yue

et al. 2021

AATCC Journal of Research

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Color is the foundation of well-put-together ensembles in clothing collocation. The way people coordinate and match different hues within looks may bring different visual feelings. However, clothing color collocation is an extremely challenging task due to great changes of public tastes and differences of consciousness towards color collocation. We propose a clothing color collocation model that consists of two deep neural networks based on color histograms calculated from a quantized color space and design features from clothing images. Given a clothing collocation, the model produces a score that indicates the degree that these clothes match. Experiments on Polyvore409k data sets validate the proposed model for clothing color collocation.

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POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

Cited by 3 publications

References 20 publications

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Leveraging Two Types of Global Graph for Sequential Fashion Recommendation

Clothing Color Collocation with Deep Neural Networks

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