Alie’s “End-User Interaction” recommendations rely on the user's past interaction with your platform which could be his/her likes, purchases, views, ratings, or browsing history.
Alie uses content-based filtering to understand a user’s behavior with the item’s attributes, which he/she reacts positively to. Once the system understands the behavior, a link is established between Users, Items & their attributes, and recommendations are made accordingly. For Ex. If a User ‘A’ has watched a movie named ‘M1’ of genre ‘action’, then Alie will check other movies of similar content or genre action and resulting items will be shown as commendations to the user.
Alie’s “End-User Interaction” recommendations are independent of the actions performed by other users and rely more on item/content data. This type of filtering works to establish a similarity index between items, once the similarity is established Alie prioritizes the items to be recommended which are most similar.
The cold-start problem occurs when items added to the catalog have very few interactions. This creates a problem for collaborative filtering algorithms because they rely on user-item interactions to make recommendations. Alie’s End-User Interaction algorithm is designed in such a way that it helps in reducing the cold start issue. Alie Recommendation Platform can start showing similar item recommendations even if there are very few user-item interactions. For Ex. If a user is signing up on an ecommerce site to buy a product then based on his purchase Alie can start recommending similar items.
Alie’s “End-User Interaction” 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 “End-User Interaction” algorithm in 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.