We offer complete recommendation system based on:

• Content Based Based on the assumption that people like items with similar attributes or features.
• Collaborative:
  • User – User
  • Item – Item Based on the assumption that people who like similar things in the past are likely to like similar things in the future.
• Hybrid Combination of above recommendation types, e.g. 50/50.

Content Based

Recommends items based on the similarity between the attributes or features of the items. Algorithm recommends products which are similar to the ones that a user has liked in the past.

Collaborative / User - User

The algorithm finds the similarity score between users and picks out the most similar users and recommends products which these similar users have liked or watched previously. Not effective when number of users is much bigger than number of content.

Collaborative / Item - Item

Algorithm finds the similarity between each movie pair and based on that, we will recommend similar movies which are liked by the users in the past. Works well when number of users is much bigger than number of content.

Hybrid Setup

Hybrid recommendation system combines all above approaches - is a mixture of Content and Collaborative based systems. Among other advantages it also resolves User and Product Cold starts issues.