Capillary Hackathon - Recommender system for fashion retail. Develop an algorithm which will recommend best suited items from inventory to a user in order to improve his/her shopping experience.
- implicit 0.3.8
- pandas 0.23.4
- scipy 1.2.0
- tqdm 4.29.1
User-Item matrix(R) was decomposed into lower dimensional user factors(U) and item factors(V). By randomly assigning the values in U & V, using alternating least squares iteratively U & V were computed such that we arrive closer to R = U x V.
Then, using transaction data, last purchased item was taken for each of the users and top 10 items were recommended accordingly based on item-item similarity scores.
To be updated soon.
[1]https://medium.com/radon-dev/als-implicit-collaborative-filtering-5ed653ba39fe