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Enhancing Recommendation System using Adapted Personalized PageRank Algorithm

With the wide spread of movie platforms and the diversity in the quantity and quality of movies shown, it is difficult for Movie audiences to find related information about the movies that they may be interested in. And have contributed to finding a way to provide a list of movies that suits the user’s desire and personalization. In this study, we present an enhancement to the recommendation system by exploiting the personalized PageRank algorithm to find the most personalized candidate list of movies for the target user. PageRank Algorithm is a useful method for the recommendation task. PageRank algorithm is used to improve the representation of movies and users in the graph network in addition to ranking movies for a target user in the recommendation activity. User preferences have been calculated based on movie genres as a part of building the user profile from his history of rating (content analysis), and for each user, this preference was used to produce a dynamic initial rank value for each movie instead of the traditional static initial rank value. In addition to personalizing the target user, a new way has been presented to personalize each user with different weights according to their rating on movies, by supporting the proposed recommender system with new personalization parameters for each user. To evaluate the performance of the proposed recommender system and measure the accuracy of recommendations, precision, and recall metrics have been used. The experimental results show that there is a significant improvement in the recommendations process, which means the hybrid recommender system using personalized PageRank (HRS-PPR) is better than the traditional recommender system using personalized PageRank. The experimental results show that the proposed approach enhances the accuracy of the recommendation regarding precision and recall measures.