Capillary Machine Learning Hackathon

competition link

Mean average precison @ 10 (MAP@10) : 0.0303297169

Private Leader Board Rank : 27

Analysis and data Preparation Notebook

Final model Notebook

Libraries used:

• pandas 0.22.0
• numpy 1.14.6
• matplotlib 3.0.2
• seaborn 0.7.1
• implicit 0.3.8
• keras 2.2.4
• opencv-python 3.4.5.20
• Pillow 4.0.0

Solution

• Here in this competition we are only evaluating model based on existing customers.
• We have more than 25000 users and 3000 products.
• We have to recommend (predict) top 10 products which a user is going to buy in last two months.
• Images of all the product and their attributes are given.

Features

• Created features for all product based on their attribute values. Total 243 features sparse features are created.
• From Keras used DenseNet121 ImageNet pretrained model to create features from product images. Total 256 features.
• So, now product has total 499 features.

Approach / Models

• Used 2 methods,

1. Collaborative filtering

• Based on user-user similarity matrix and cosine similarity predicted score for all user-product pair

2. Content based filtering

• All the product related attributte features and Image feature are used.

• First user profile matrix has to be created, so that we can compare that to all the products and find best similar product for user.

• Weighted average of all the product features are calculated to make profile vector for a user, whcih is based on how many times perticular product is bought by the user.

• Now based on cosine similarity between user profile vector and all product vector, find best 10 similar products.

• Content based filtering gave higher score than user-user collaborative filtering.

• This indicates that user are more likely to buy products similar to what they have already bought.

• Tried Hybrid approach (Combine collaborative and content based filtering), but it didn't worked, so I have used content based filtering model as a final model.