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Ant colony algorithms have become recently popular in solving many optimization problems\udbecause of their collaborative decentralized behavior that mimics the behavior of real ants\udwhen foraging for food. Recommender systems present an optimization problem in which they\udaim to accurately predict a user’s rating for an unseen item by trying to find similar users in the\udnetwork. Trust-based recommender systems make use of trust between users. T-BAR was the\udfirst successful application of an ant colony algorithm to trust-based recommender systems but\udit lacked the ability to deal with cold start users. In this paper we propose a dynamic trust-based\udant recommender (DT-BAR) that solves the problem of cold start users by locally initializing the\udpheromone level on edges using the dynamically changing information within each\udneighborhood as ants pass by. DT-BAR increases the communication between ants and\udemphasizes the importance of trust in the pheromone initialization process

DT-BAR : A Dynamic ANT Recommender to Balance the Overall Prediction Accuracy for all Users