Identifying products a specific customer likes most can significantly increase the earnings of a company. For that purpose, a neural network classifier is used as a data-driven, visually-aware feature extractor. The latter then serves as input for similarity-based recommendations using a ranking algorithm. Combined with more traditional content-based recommendation systems, image-based recommendations can help to increase robustness and performance, for example, by better matching a particular customer style. In this hack session, learn how to build content based recommender systems using image data.
Key Takeaways:
Understanding of Recommender Systems
- Collaborative Systems.
- Content Based Recommender Systems.
- Deep Learning Algorithms for Unsupervised Computer Vision
Convolutional Neural Networks (Convolution, MaxPooling, BatchNorm)
- Transfer Learning for CNN Architectures
- Inception Models
- RESNET Models
- VGG Models
Understanding Similarity Measures
- Euclidean Distance measures
- Cosine Similarity measures
Building an End to End Content Based Recommender System