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Recommender systems have been one of the most prominent information filtering techniques during the past decade. However, they suffer from two major problems, which degrade the accuracy of suggestions: data sparsity and cold start. The popularity of social networks shed light on a new generation of such systems, which is called social recommender system. These systems act promisingly in solving data sparsity and cold start issues. Given that social relationships are not available to every system, the implicit relationship between the items can be an adequate option to replace the constraints. In this paper, we explored the effect of combining the implicit relationships of the items and user-item matrix on the accuracy of recommendations. The new Item Asymmetric Correlation (IAC) method detects the implicit relationship between each pair of items by considering an asymmetric correlation among them …