<p class="Abstract">Recommender systems help users to extract useful information from a large volume of complex data, and their use has received significant attention in recent years. In practice, the interests of users and the characteristics of items in these systems
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<p class="Abstract">Recommender systems help users to extract useful information from a large volume of complex data, and their use has received significant attention in recent years. In practice, the interests of users and the characteristics of items in these systems change over time, and therefore, adapting recommender systems to these types of changes is necessary and helps to provide more accurate recommendations to users. However, most temporal recommender systems are only based on the dynamics of users' preferences over time and do not consider changes in item characteristics.</p>
<p class="Abstract">In this paper, we propose a non-negative matrix factorization-based recommender system that uses both dynamics of users' interests and the changes in item characteristics over time in predicting users' ratings of items. In the proposed model, in order to reduce the data sparsity problem, in addition to users' ratings, trust between users is also used. The evaluation results on the Epinions dataset show that the proposed model is more accurate than the compared methods.</p>
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