Community Trend Prediction on Heterogeneous Graph in E-commerce

Abstract

In online shopping, ever-changing fashion trends make merchants need to prepare more differentiated products to meet the diversified demands, and e-commerce platforms need to capture the market trend with a prophetic vision. For the trend prediction, the attribute tags, as the essential description of items, can genuinely reflect the decision basis of consumers. However, few existing works explore the attribute trend in the specific community for e-commerce. In this paper, we focus on the community trend prediction on the item attribute and propose a unified framework that combines the dynamic evolution of two graph patterns to predict the attribute trend in a specific community. Specifically, we first design a communityattribute bipartite graph at each time step to learn the collaboration of different communities. Next, we transform the bipartite graph into a hypergraph to exploit the associations of different attribute tags in one community. Lastly, we introduce a dynamic evolution component based on the recurrent neural networks to capture the fashion trend of attribute tags. Extensive experiments on three real-world datasets in a large e-commerce platform show the superiority of the proposed approach over several strong alternatives and demonstrate the ability to discover the community trend in advance. CCS CONCEPTS• Applied computing → Electronic commerce; • Information systems → Evaluation of retrieval results.

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

“…FastkNN [152] FCNN [56] FNN [32] Fuzzylogic [106,113] GAN [71,75,99,129,134,141,155,251] GA-RF [65] GaussianMixtureModels [109,118] gradientboostingdecisiontree [42] GraphCNN [40,122] GraphDCNN [89] GRU [230] GSN [87] H-CNN [39,157] HOG [153] HypergraphNN [80] Imageprocessing [119,120,130,132,135,136,147] k-center [117] k-means [114,203,232,244] k-medoids [115] Kneser-Ney [34] kNN [54,137,222,235,237,250] LAC [107,116] Lassoregression …”

“…These studies aim to enhance the annotation process's object identification, segmentation, and classification algorithms [34,35,39,40,. Another group of studies develops techniques for advising and helping customers by pairing products or examining the textures of garments [78][79][80][81]84,[121][122][123][124][125][126][127][128]. Additionally, some authors [32, are developing virtual reality technology to help customers with clothing fitting.…”

“…Recommender [201] Regressionmodels [142,174] RepTree [203] review [121,138,188,190,204,248] RFM [207] R-GCN [242] RNN [80] Robotandpressuremeasurements [146] SEM [239] siamesenetwork [46,75,79,156] SSD [35,92] Survey [53,127,143,150,162,181,191,196,210,219,221] Survey:kanomodel [139] SVM [64,87,153,179] SVM.REPTree [172] SVP [222] UCB [233] VAR [207] VGG-IE [65] Viola-Jones [148] word2vec [98,102,167] word2vecSVMperf [180] XGBoost [176] Table 6<...…”

“…Taobao [80] TaobaoiFashion [122] ThePraguetexturesegmentationdata-generatorandbenchmark [118] Tianchi [159] Virtualfitting [137,147,148] VITON [134,140] WFID [86]…”

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

“…Numerous studies have been conducted to extract features from unstructured data, such as photos, audio, text, and others, using deep neural networks. [8].…”