(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 7, 2022 Recommendation System based on User Trust and Ratings Mohamed TIMMI 1 , Loubna LAAOUINA 2 , Adil JEGHAL 3 , Said EL GAROUANI 4 , Ali YAHYAOUY 5 LISAC Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco 1, 4, 5 LISA Laboratory, National School of Applied Science, Sidi Mohamed Ben Abdellah University, Fez, Morocco 2 LISAC Laboratory, National School of Applied Science, Sidi Mohamed Ben Abdellah University, Fez, Morocco 3 Abstract—Recommendation systems aim at providing the user with large information that will be user-friendly. They are techniques based on the individual’s contribution in rating the items. The main principle of recommendation systems is that it is useful for user’s sharing the same interests. Furthermore, collaborative filtering is a widely used technique for creating recommender systems, and it has been successfully applied in many programs. However, collaborative filtering faces multiple issues that affect the recommended accuracy, including data sparsity and cold start, which is caused by the lack of the user's feedback. To address these issues, a new method called “GlotMF” has been suggested to enhance the collaborative filtering method of recommendation accuracy. Trust-based social networks are also used by modelling the user's preferences and using different user's situations. The experimental results based on real data sets show that the proposed method performs better result compared to trust-based recommendation approaches, in terms of prediction accuracy. Keywords—Recommendation systems; collaborative filtering; trust; social networks I. INTRODUCTION The platforms with thousands of items will support the users to be able to know how to connect to the right content, which is relevant to their interests and concern. To help users, the systems of recommendation emerge as a great solution to personalize the content presented to the users in the form of techniques and software tools that provide personalized suggestions and recommendations for items in order to boost the users' competencies [1, 2]. Even though several types of methods have been proposed to build systems of recommendation, the collaborative filtering method remains one of the greatest widely used and adopted techniques to generate recommendations. It is far from ideal in terms of predictive performance. As, it suffers from countless inherent problems [3, 4]. The most important thing of these is data sparsity and cold start, which affects the recommender's accuracy of the system [3]. To address these issues and model the user's preferences more accurately, the additional information can be incorporated into the collaborative filtering method to compensate for insufficient rating information, such as social media information, including friendship, belonging, and trusting relationships [1, 5, 6]. The relationship which is based on trust is one of the most crucial types of social relationships, as it gives its power and good positive association with similarities between the users [1], and several studies have shown great efficiency in improving predictive accuracy compared with the traditional recommendation techniques. Additionally, collaborative filtering is one of the most common approaches in systems of recommendation. As it does not depend on additional data, only the history of interactions, it becomes quite simple to be reproduced in various real applications and increase its popularity. The recommendation based on collaborative filtering was developed from the observation that people tend to adopt other people's recommendations. Someone who has the intention to purchase a certain product, for example, s/he looks for opinions and points of view from the other people who have already purchased and bought the same product before deciding to purchase. This happens frequently in the daily lives of people with different yet varied situations. The selection of a certain movie, a book, among many other [2]. Several trust-based systems of recommendation that employ these models to solve data sparsity and cold-start problems have also been proposed to combine the impacts and the great influences of social trust with different strategies. However, the previous work which is proposed in this field failed to systematically model the reciprocal effect between the users. It cannot model how and to what extent the user's preferences are affected by trustees and at the same time to what extent it influences the same user by trustors, where the user preferences as trustee or trustor can be distinct from each other [7]. Therefore, when predicting a user's preferences for an item, it does make more sense to consider both the trustor's preferences and the trustee's performances at the same time. However, in the previous studies, the methods modelled the users using a single case [8], or by separately considering the two user cases [7]. In other words, no distinction is made between different cases of the user as trustee or trustor in the ratings generation process. Regardless of the learning approach adopted by the systems of recommendation, there must be a past set of interactions that describe the users' relationships with the items of the system. Past interactions between a user and an item are traditionally called feedback, and they can be either explicit or implicit. Most of the existing methods depend on an explicit trust bond between the users, based on which users display their preferences as trustors or trustees, except those users who may not explicitly interact with others, but rather implicitly. We note that most of the methods found in previous studies are effective and efficient in modelling explicit relationships. However, they do not consider the discovery and modelling of implicit interactions between two users who may be similar but not connected in the network of trust. The local perspective of 174 | Page www.ijacsa.thesai.org