Fashion coordinates recommendation based on user behavior and visual clothing style

Gu, Sida; Liu, Xiaoqiang; Cai, Lizhi; Shen, Jie

doi:10.1145/3162957.3162982

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…Recommenders for fashion products have been sought after to improve customer satisfaction [9][10][11]15,16,[53][54][55][56][57][58]. The fashion product recommenders usually use interaction logs between users and products and visual features [8,15,16]. In relation to visual features, an interesting characteristic of fashion recommenders is that systems consider fashion coordination between multiple products whether it is based on interaction logs or visual features [7][8][9]15,[59][60][61].…”

Section: Sustainable Marketing and Recommenders For Fashion Productsmentioning

confidence: 99%

“…The fashion product recommenders usually use interaction logs between users and products and visual features [8,15,16]. In relation to visual features, an interesting characteristic of fashion recommenders is that systems consider fashion coordination between multiple products whether it is based on interaction logs or visual features [7][8][9]15,[59][60][61]. When choosing clothes, people consider various aesthetic factors such as color, type, material, style and season [60].…”

Section: Sustainable Marketing and Recommenders For Fashion Productsmentioning

confidence: 99%

“…They used this to extract the aesthetic features of clothing from images. Gu et al [8] suggested a deep learning modeling algorithm, called Autoencoder, which differs from CNN and used it to extract features from fashion product images. He and McAuley [58] also used CNN for extracting visual features as well as temporal dynamics.…”

Section: Sustainable Marketing and Recommenders For Fashion Productsmentioning

confidence: 99%

“…ABRs, such as Content-Based Recommenders (CBR) and Knowledge-Based Recommenders (KBR), use item attributes to create recommendation models. [7][8][9][10]. In general, fashion item attributes, such as brand, color, price, season and style, are important in fashion recommendation, because they significantly affect users' preference.…”

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confidence: 99%

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Session-Based Recommender System for Sustainable Digital Marketing

Hwangbo

Kim

2019

Sustainability

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Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

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Section: Sustainable Marketing and Recommenders For Fashion Productsmentioning

confidence: 99%

Section: Sustainable Marketing and Recommenders For Fashion Productsmentioning

confidence: 99%

Section: Sustainable Marketing and Recommenders For Fashion Productsmentioning

confidence: 99%

mentioning

confidence: 99%

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Session-Based Recommender System for Sustainable Digital Marketing

Hwangbo

Kim

2019

Sustainability

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“…It extends latent factor model (LFM) to handle user behavior characteristics and uses the denoising autoencoder network model to process visual features. By combining the two models, the recommendation accuracy is effectively improved and the cold start problem is solved [14].…”

Section: Related Work a Fashion Recommendationmentioning

confidence: 99%

Differentiated Fashion Recommendation Using Knowledge Graph and Data Augmentation

Yan

Zhou

2019

IEEE Access

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E-commerce recommender systems (RSs) can help users quickly find what they need or new products they might be interested in. To continuously enhance user trust in the website, improve page visits and dwell time, and most importantly, increase gross merchandise value (GMV), it is crucial to understand and capture the important information hidden in the data, which has a great impact on user choice. The fashion e-commerce websites can collect the attributes of items and users as well as the user purchase behaviors, but lack the fine-grained classification of the items and the implicit relationship between items and users. This paper focuses on Amazon fashion dataset, one of the most widely used datasets in the fashion field. A differentiated recommendation framework is proposed that provides different recommendation paths for active and inactive users to improve the overall recommendation quality. In the framework, a data augmentation algorithm based on transfer learning is proposed to filter out the irrelevant items and label items with fine-grained tags, and a user-item knowledge graph is built to discover the potential relationship between items and users. Finally, a differentiated recommendation strategy is put forward to make different recommendations for users with different characteristics. The experimental results show that through data augmentation algorithm to improve data quality, factorization machine model produces higher recommendation accuracy, the constructed knowledge graph can alleviate the cold start problem for recommendation, and the differentiated recommendation strategy has achieved better recommendations for both active and inactive users.

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Sequence aware recommenders for fashion E-commerce

Kim

Hwangbo

Lee

et al. 2022

Electron Commer Res

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In recent years, fashion e-commerce has become more and more popular. Since there are so many fashion products provided by e-commerce retailers, it is necessary to provide recommendation services to users to minimize information overload. When users look for a product on an e-commerce website, they usually click the product information sequentially. Previous recommenders, such as content-based recommenders and collaborative filtering recommenders, do not consider this important behavioral characteristic. To take advantage of this important characteristic, this study proposes sequence-aware recommenders for fashion product recommendation using a gated recurrent unit (GRU) algorithm. We conducted an experiment using a dataset collected from an e-commerce website of a Korean fashion company. Experimental results show that sequence aware recommenders outperform non-sequence aware recommender, and multiple sequence-based recommenders outperform a single sequence-based recommender because they consider the attributes of fashion products. Finally, we discuss the implications of our study on fashion recommendations and propose further research topics.

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Fashion coordinates recommendation based on user behavior and visual clothing style

Cited by 13 publications

References 6 publications

Session-Based Recommender System for Sustainable Digital Marketing

Session-Based Recommender System for Sustainable Digital Marketing

Differentiated Fashion Recommendation Using Knowledge Graph and Data Augmentation

Sequence aware recommenders for fashion E-commerce

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