Learning Context-Aware Outfit Recommendation

Abstract

With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching.

An Analysis of the Impact of Personality Traits towards Augmented Reality in Online Shopping

“…The hypotheses were tested with bootstrapping of 5000 resamples. To analyse the mediation effects, we used the recommendation of bootstrapping found in [70,71], and interpretation of the results was carried out using the guidelines recommended by [8,72,73]. The analysis of VIF values (the variance inflation factor) for the assessment of multicollinearity showed VIF values lower than 4, suggesting that collinearity is not an issue.…”

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

“…The ASTs are broken down into small features using preprocessing steps and the Bag of Words (BoW) model. Preprocessing steps [35][36][37] comprise eliminating of stop words, stemming, lowest and highest occurrences, and so forth. The stop words are the noisy data that may not convey valuable information.…”