Mixed-Based Approach for an Intelligent E-Commerce Recommender System

Content-based recommender systems and collaborative filtering recommender systems might be useful in recommending items that might be preferred by users based on their preferences from the past, these systems rely on users’ preferences, items’ ratings, and items’ descriptions. Thus, these systems might be less useful for items for which users’ preferences and ratings cannot be easily collected. On the other hand, a constraint-based recommender system can suggest items that fulfil users’ requirements (or constraints) by relying on users’ requirements and knowledge about the domain of the items for giving a recommendation. In the case where, no item can be found, the system will proceed with the constraints of a relaxation phase to search for possible modifications in the constraints, give advice to the user, and enable them to edit their requirements to obtain more results. In this study, we propose a new approach called a mixed-based approach. The approach is based on an integration of soft and hard relaxation-based approaches. The proposed approach is implemented as a notebook recommender system. We conducted experiments on the proposed approach using collected data from e-commerce websites. The results also show that the proposed mixed-based approach is able to control constraints, and gives better performance in giving recommendations to users.