Alie’s “other user’s choice” recommendations rely on the likes and dislikes of other users. Alie uses collaborative filtering & deep learning, to identify patterns among user actions and compute a similarity index between the users. The similarity index helps in establishing the link between users, which in turn enables Alie to suggest accurate personalized recommendations for every user. For Ex. User A & B have seen a movie named ‘M1’ and they both liked it, if User A watches another movie named ‘M2’ then movie ‘M2’ will be shown as a recommendation to User B.
This algorithm will enable you to recommend items to your end-users that have been purchased, reviewed, or liked by other users having similar tastes & preferences. The performance of the “other user’s choice” algorithm keeps improving as more and more similar users and their choices get discovered in real-time.
Other user’s choice recommendations are based on user-based collaborative filtering methods. This type of filtering is context-independent, so it gives diversified results compared to item-based filtering which needs a clear context to establish the relation. For Ex. If User A has purchased a Mobile from an Ecommerce Site, then based on item-based filtering a headphone is recommended to User A. If User A is already using a headphone then this recommendation may not be relevant to him.
Other User's Choice algorithm is easy to implement. All you need to do is just login into Alie CMS, create a new project, add data using APIs or JS Plugins, select the “other user’s choice” algorithm on the next tab and generate the final output (Recommendations). Subsequently, you can hook these recommendations on the appropriate pages of your website or app using APIs provided by Alie.