A Context-Aware Recommendation System with Effective Contextual Pre-Filtering Model

Informational resources have significantly expanded as a result of the growth of the internet. Consequently, making personalized suggestions about different types of information, goods, and services is the best strategy to assist customers in solving the issue of information overload. As a result, recommendation systems are employed to aid clients in locating the products most appropriate to their interests. The majority of traditional recommender systems rely on a traditional model that just takes into account user-item-rating interactions without taking context into account. It has been demonstrated that context-aware recommender systems deliver improved predicted performance across a variety of areas by attempting to adapt to users' preferences across various settings. This study presents a proposed system to help the recommender system solve its difficulties in producing accurate predictions that are relevant to the user's preferences. The system is the Contextual Pre-filtering Based Collaborative Filtering (CPBCF) model, which is based on splitting items. To decrease the time and space needed for processing correlations, it depends on the recommended splitting approach utilizing the variance equation, which decreases the dataset depending on the most important attributes. In the proposed system experiments, the performance of CPBCF with and without contextual pre-filtering was enhanced by (5-7%) for the precision, (7-8%) for the recall, and (7-8%) for the f1-measuer. While the complexity time has enhanced by (3-4 sec). The effectiveness of the CPBCF model was evaluated using various numbers of neighbors. We can observe that neighborhood size does have an effect on forecast accuracy.