Computer implemented method for providing a recommender system for use in the design process of a complex system being an electric or electronic circuit or an ASIC or a circuit having one or more FPGAs or a processor or a SoC or an embedded system whereby the recommender system is generated by use of a model of the complex system, said model based on a graph neural network, for said model, the complex system being described by nodes representing components of the complex system and edges representing connections between the components, wherein the design process comprises design steps in each of which at least one item is added to a source node of a partial design, whereby said recommender system calculates a ranked list of all candidate items (x i) out of a variety of items and provides to a user a subset of the ranked list as recommendation which item to add to the source node in a subsequent design step, said providing of the recommendation comprising the following steps: a) Determining for each item (x i) a calibrated score(s i) indicating a likelihood that the specific item will be added in the subsequent design step; b) Applying a threshold with regard to the score; c) Determining whether at least one item (x i) has a score (s i) above the applied threshold; d) If there is no item (x i) with a calibrated score (s i) above the threshold, then using information whether at least one property (p) of the items (x i) is desired; e) Forming a subset of items possessing the desired property (p); f) Performing, by the recommender system, on the subset a computation of the calibrated scores (s i) of the items contained in the subset; g) Applying a threshold for the subset; h) Using the ranked list of the subset as ranked list if at least one item (x i) thereof has a calibrated score (s i) above the threshold applied for the subset and providing to the user the part of the ranked list above the threshold as recommendation of items to be added in the next design step. whereby the recommender system is generated by use of a model of the complex system, said model based on a graph neural network, for said model, the complex system being described by nodes representing components of the complex system and edges representing connections between the components, wherein the design process comprises design steps in each of which at least one item is added to a source node of a partial design, whereby said recommender system calculates a ranked list of all candidate items (x i) out of a variety of items and provides to a user a subset of the ranked list as recommendation which item to add to the source node in a subsequent design step, said providing of the recommendation comprising the following steps: a) Determining for each item (x i) a calibrated score(s i) indicating a likelihood that the specific item will be added in the subsequent design step; b) Applying a threshold with regard to the score; c) Determining whether at least one item (x i) has a score (s i) above the applied threshold; d) If there is no item (x i) with a calibrated score (s i) above the threshold, then using information whether at least one property (p) of the items (x i) is desired; e) Forming a subset of items possessing the desired property (p); f) Performing, by the recommender system, on the subset a computation of the calibrated scores (s i) of the items contained in the subset; g) Applying a threshold for the subset; h) Using the ranked list of the subset as ranked list if at least one item (x i) thereof has a calibrated score (s i) above the threshold applied for the subset and providing to the user the part of the ranked list above the threshold as recommendation of items to be added in the next design step. a) Determining for each item (x i) a calibrated score(s i) indicating a likelihood that the specific item will be added in the subsequent design step; b) Applying a threshold with regard to the score; c) Determining whether at least one item (x i) has a score (s i) above the applied threshold; d) If there is no item (x i) with a calibrated score (s i) above the threshold, then using information whether at least one property (p) of the items (x i) is desired; e) Forming a subset of items possessing the desired property (p); f) Performing, by the recommender system, on the subset a computation of the calibrated scores (s i) of the items contained in the subset; g) Applying a threshold for the subset; h) Using the ranked list of the subset as ranked list if at least one item (x i) thereof has a calibrated score (s i) above the threshold applied for the subset and providing to the user the part of the ranked list above the threshold as recommendation of items to be added in the next design step.