Content-based book recommendations: Personalised and explainable recommendations without the cold-start problem

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base ...

Double-sided recommendations (DSR) have been recently introduced for an item and a group that the item is destined for. Herein we present an algorithm which takes inspiration from the Social Comparison Theory to recommend items that had an average ...

The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...