Weekend Reading - Fashion Research Paper Reads # Part 1

Paper #1 - Outfit Recommender System

Perceptions

• Christian funeral, wearing black conservative clothes is customary
• Hindu funeral, wearing white conservative clothes is the norm

A recommender system is used to suggest products to customers by using information about the customer. Feature Descriptors - Histogram of Oriented Gradients (HOG), Speeded Up Robust Feature (SURF), Scale Invariant Feature Transform (SIFT)

• Event Analysis using Detected Objects
• Fifty-three categories of clothes

1. Multilabel classification and cloth detection
2. Approach Events - Clothes Mapping
3. Event to Outfit Mapping

Crop the images to reduce the background information

Paper #2 - Artificial Intelligence and the Fashion Industry

• Datafication of fashion - data generation through the digitization of content,and monitoring of activities, including real world activities and phenomena,through sensors
• Image Search. Personalized shopping is also achieved using AI applications based on computer vision and augmented and virtual reality.
• Reverse image search instead is the process by which an image is used to find another image.
• Visual search, a subset of reverse image search, refers to the possibility of finding items within an image and searching for those.

Paper #3 - Recommendation based on multiproduct utility maximization

Multi-product utility maximization (MPUM) - integrates the economic theory of consumer choice theory with a personalized recommendation

Two products could be substitutes - buy A instead of B or complements - buy A together B. Identifying and making use of such relationships are useful for recommendation systems.

We assume user make choices to maximize multi-product utility and use the multinomial consumer choice model for that, then the multi-product utility model can be learned to maximize the likelihood of observed user data

Collaborative filtering is based on the assumption that users with similar tastes for previous items will have similar preferences for new items

Content-based filtering is based on the assumption that the features (meta data, words in the description, price, tags, visual features, etc.) 

Hybrid recommendation algorithms combine collaborative filtering with content-based filtering

Code - Link1, Link2

Keep Learning!!!