In this work, we used the notion of Rank Aggregation (RA) to develop a software prototype for Institute Recommendation System (IRS). Specifically, the objective is to devise an institute recommendation system that takes diverse rankings from various institute ranking websites as inputs and use cognitive functions to collate and aggregate them such that the resulting ranking is more consensus and reliable. Since the institute rankings provided by different academic ranking websites are partial lists, existing full-list based algorithms fail to provide a consensus ranking. In this regard, we proposed fuzzy Shimura Preference Order Rank Aggregation (SPORA) algorithm that works efficiently for both partial as well a full list. The notion is to integrate subjective measures prevalent in realworld rankings. Though obtaining an ideal ranking is computationally NP hard, the validity of the proposed aggregation algorithm is ascertained by evaluating the resultant rankings at multiple precision points (at top-10, at top-20 and at top-100 positions) using Normalized Modified Correlation Coefficient (NMCC). The performance of the SPORA function is further evaluated by comparing the values of the NMCC with the existing baseline algorithms. Results reflect the soundness of the proposed algorithm over the existing counterparts and prove productive when used (to be used) for developing any recommendation software.