In general, judgments of plausibility are made after a claim has been formulated, but prior to rigorous testing or proof.
Zi Huang In our daily life, how to match clothing well is always a troublesome problem especially when we are shopping online to select a pair of matched pieces of clothing from tens of thousands available selections. To help common customers overcome selection issues, recent studies in the recommender system area have started to infer the fashion matching results automatically.
Due to the high complexity of visual features and the lack of historical item purchase records, most of the existing work is unlikely to make an efficient and accurate recommendation.
To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing. Its main objective is to learn meaningful yet compact high-level features of clothing items, which are represented as binary hash codes.
In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones.
The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed. The experiments compared with several state-of-the-art approaches have evidenced the superior performance of the proposed approach on efficient fashion recommendation.
The binary codes can facilitate to represent and search of massive data because it only needs about one hundred binary bits to represent one data item, and binary computation in Hamming space is efficient by using the bit operations. Many learning-based hashing algorithms have been developed to different scenarios, including the unsupervised methods supervised methods , deep learning based hashing methods .
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