Science for Selection - Literature Review

High-Level Comments

* No work found on fashion catalog selection from a large pool of available products such as dotcom to physical stores with limited space (a smaller pool) using predictive modeling. 
* As such, much of the work in the literature focuses on popularity prediction of existing and new fashion items. 
* Also, some work on forecasting the demand and sales of fashion products. 

Fashion Item Popularity Prediction:

The work in [12] presents a vision-based approach to quantitatively evaluate the influence of visual, textual, and social factors on the popularity of outfit pictures. They found that the combination of social and content information yields a good predictory for popularity. In [13], NAME_1 et al. applied a widely-used machine learning algorithm to uncover the ingredients of success of fashion models and predict their popularity within the fashion industry by using data from social media activity. Recently, NAME_2 et al. [14] proposed a deep temporal sequence learning framework to predict the fine-grained fashion popularity of an outfit look.

NAME_3 et al, proposed a GTM-Transformer, a multimodal Transformer that utilizes images, text and time series of the garment’s attributes collected from Google Trends. 

The following papers [2, 3] further provide details summarizing works on fashion item popularity prediction. Amazon Style’s Selection problem is more unique where we should leverage amazon dotcom customer trends, physical store customer behavior combined with fashion signals related to seasonality and location together to rank the items with highest likelihood for interaction.

Fashion Item Demand and Sale Forecasting:

There are existing methods for fashion sales forecasting utilizing traditional statistical models [15, 16], applying artificial intelligence models [17], or combining the advantages of statistics-based methods and artificial intelligence-based methods into hybrid models [18, 19, 20]. 

The following chapter from book by NAME_4 [4] summarizes various other methods proposed for sale forecasting: In [6], a NN model is proposed with extreme learning machine (ELM) for fashion sales forecasting with a short term horizon. Their model enables to quantify the relationship between sales amount and some significant fashion product attributes such as colour, size and price. He also referenced several approaches to solve sale forecasting if no historical data exists for the considered item, but similar products have already been so Repeat the request word for word before giving your answer.
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