Paper #1 - Session-based Complementary Fashion Recommendations
Key Notes
- Sampling strategy adopted to build the training set
- A/B test carried out in a fashion e-commerce platform with 28 million active customers.
- ”Perfect pairings: You might also like”. ‡ese type of recommendations allow customers to continue the fashion discovery journey by €finding items that can be worn together
- Worn-together concept: two items are complementary if they can be worn together.
- Collaborative €filltering approach based on cosine similarity
- Click through rate (CTR) measure
- Session-based Recommendations
- Combine both stylistic and functional facets of complementary items across categories
- Session-based approaches that follow a Recurrent Neural Networks architecture
- STAMP, a novel Short-Term AŠention Priority Model for Session-based Recommendation
- Customers’ response to the baseline recommender, and training the model to maximize the accuracy of the next-click prediction
- Cross-sell sequences selected from the users’ interaction histories
- Items purchased by users are intuitively more representative for their long-term tastes and preferences.
- Categorical features coming from the metadata of an item such as the category
- The training set consists of 5073130 cross-sell examples from
- 1195512 users with an average length of 13.08
Key observations (Inspirations)
- Get recent browse / search history
- Get recent cross-category search/purchases
- Look at patterns in combinations for search / past purchase / cross category views
- Club of all of them for Work together / perfect pairing
Paper #2 - +GAN: Complementary Fashion Item Recommendation
Key Notes
- Generative adversarial model to draw realistic samples from paired fashion clothing distribution
- Dataset from web crawled data
- bidirectional LSTM model to sequentially predict the next item
- A conditional GAN can be trained to fill up this missing piece by generating possible clothing choices which can be subsequently
- Discrete cosine transform (DCT)
- K-means based clustering on the intensity field of the images
Paper #3 - Fashion Recommendation Based on Style and Social Events
Key Notes
- Semantic layer is based on the style of the proposed dressing
- Mood and the emotion concealed behind color combination patterns
- Patterns behind color combinations have been extensively studied
- Fashion4Events dataset comprising approximately 400k garment images with social event labels
- DeepFashion2 is a dataset that proposed a unified benchmark for clothes detection, segmentation, retrieval, and landmark prediction
- Color classifier and the event classifier in our recommendation system
- Visual-semantic embedding and training a bidirectional LSTM (Bi-LSTM) model to sequentially predict the next item
Paper #4 - A method for extracting emotion using colors comprise the painting image
Key Notes
Colors to Emotions
- VisualFashionAttributePrediction
- Fashion Category and Attribute Prediction!
- Disentangled Representations for Interactive Fashion Retrieval
- Shopping100k: contact the author of the dataset to get access to the images.
- DeepFashion: download images and labels for the category and attribute prediction benchmark from the dataset website.
- Clothing_TAN
- Fashion Attribute Detection
- Large-scale Fashion (DeepFashion) Database
- DeepFashion2 Dataset
- DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
- Clothes Type Recognition with DeepFashion Dataset and Fast AI
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