A processor implemented method for recommender model selection, comprising: collecting user requirements as input, via one or more hardware processors, wherein the user requirements comprise at least one error measure and corresponding at least one error value and error tolerance; processing the collected user requirements using a recommendation learned model pre-trained on information pertaining to capability of a plurality of recommender models and user requirements, via the one or more hardware processors, wherein processing the collected user requirements comprising: determining dynamically, value of EV and ET of the at least one error measure for each of the plurality of recommender models; determining correlation between the determined value of EV and ET of the at least one error measure for each of the plurality of recommender models with the EV and ET in the user requirement, via the one or more hardware processors; determining at least one recommender model among the plurality of recommender models as a recommender model matching the user requirement, based on the determined correlation, via the one or more hardware processors; training the recommendation learned model by using information pertaining to capability of the plurality of recommender models and the user requirements as training data, wherein the recommendation learned model is updated using real time data and wherein the training data specifies the user requirements, corresponding EV and ET, specifications and capabilities of the plurality of recommender models, via the one or more hardware processors; and generating a recommendation based on the at least one recommender model determined as matching the user requirement, via the one or more hardware processors. collecting user requirements as input, via one or more hardware processors, wherein the user requirements comprise at least one error measure and corresponding at least one error value and error tolerance; processing the collected user requirements using a recommendation learned model pre-trained on information pertaining to capability of a plurality of recommender models and user requirements, via the one or more hardware processors, wherein processing the collected user requirements comprising: determining dynamically, value of EV and ET of the at least one error measure for each of the plurality of recommender models; determining correlation between the determined value of EV and ET of the at least one error measure for each of the plurality of recommender models with the EV and ET in the user requirement, via the one or more hardware processors; determining at least one recommender model among the plurality of recommender models as a recommender model matching the user requirement, based on the determined correlation, via the one or more hardware processors; training the recommendation learned model by using information pertaining to capability of the plurality of recommender models and the user requirements as training data, wherein the recommendation learned model is updated using real time data and wherein the training data specifies the user requirements, corresponding EV and ET, specifications and capabilities of the plurality of recommender models, via the one or more hardware processors; and generating a recommendation based on the at least one recommender model determined as matching the user requirement, via the one or more hardware processors. determining dynamically, value of EV and ET of the at least one error measure for each of the plurality of recommender models; determining correlation between the determined value of EV and ET of the at least one error measure for each of the plurality of recommender models with the EV and ET in the user requirement, via the one or more hardware processors; determining at least one recommender model among the plurality of recommender models as a recommender model matching the user requirement, based on the determined correlation, via the one or more hardware processors; training the recommendation learned model by using information pertaining to capability of the plurality of recommender models and the user requirements as training data, wherein the recommendation learned model is updated using real time data and wherein the training data specifies the user requirements, corresponding EV and ET, specifications and capabilities of the plurality of recommender models, via the one or more hardware processors; and generating a recommendation based on the at least one recommender model determined as matching the user requirement, via the one or more hardware processors.