Item recommendations via deep collaborative filtering
US10255628B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 6, 2015 |
| Grant date | Apr 9, 2019 |
| Priority date | — |
| Expiry date | Oct 20, 2037 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY04S10/50
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep collaborative filtering (DCF) approach is employed in a recommender system to provide item recommendations to users. The DCF approach combines deep learning models with matrix factorization based collaborative filtering. To provide item recommendations, a user-item rating matrix, user side information, and item side information are provided as input to a recommender system. The recommender system learns user latent factors and item latent factors by jointly: (1) decomposing the user-item rating matrix to extract latent factors, and (2) extracting latent factors from hidden layers of deep learning models using the user side information and item side information. The learned user latent factors and item latent factors are used to predict item ratings for missing ratings in the user-item rating matrix. The predicted item ratings are then used to select item recommendations for a given user, which are then communicated to a user device of the user.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.