10 2. DATA COLLECTION
user body shape-related data, which makes the dataset unsuitable for our comprehensive PCW
creation. Fortunately, we noticed that the user purchase history, especially the size of purchased
fashion items, involving specific body measurements (such as the hip girth and waist girth), con-
veys more reliable cues of the user body shape. Inspired by this, we constructed our own dataset,
named bodyFashion, by collecting user purchase histories from Amazon. In particular, we first
collected a set of popular fashion items from Amazon. After that, based on item comments, we
tracked a lot of Amazon users. We crawled their recent historical purchase records (limited by
100 records), and selected the fashion items from them. In order to guarantee the quality of the
dataset for PCW creation, we screened out users with less than 6 historical purchase records,
and then obtained 116,528 user-item records involving 11,784 users and 75,695 fashion items.
Each item comprises its image, title, and category metadata. Both purchase sizes and ratings are
available for each user-item record. A record example is illustrated in Figure 2.2.
Figure 2.2: An example of bodyFashion. Users are associated with rich labeled fashion items
they purchased.
2.4 SUMMARY
In this chapter, we introduce three real-world datasets collected from the fashion-oriented on-
line communities: Polyvore, Iqon, and Amazon, respectively. In particular, Dataset I consist-
ing of 20,726 outfits with 14,871 tops and 13,663 bottoms is collected for the general com-
patibility modeling, while Dataset II comprising 308,747 outfits created by 3,568 users with
672,335 fashion items is created for the personalized compatibility modeling. In addition, we
build Dataset III to testify the effectiveness of our model in the real-world application of per-
sonalized wardrobe creation. We have released all these datasets to facilitate other researchers in
the community of fashion compatibility modeling.
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