CDIPS_Recommender

Compare the accuracy between content-based and collaborative filter recommender system.

Content-based Recommender system

Implemented ResNet50 and convolutional neural network to extract features of product image. Recommend the most similar product based on the product image with ~60% accuracy.

Collaborative filter recommender system

1. Implemented change point detection to filter the most relevant browsing history.
2. From the browsing history, recoommend the most relevant item based on previous item view with ~60% accuracy.

Conclusion

With limited data, content-based recommender system using image features may be used as an alternative to CF recommender system.