A multi-criteria-based recommender system makes precise decisions by probing customers multiple criteria’s on-hand and gives recommendations by modelling a user’s utility for the alternatives along several criteria. This paper collects user preferences of criteria over alternatives in the form of linguistic variables. Alternatives includes 6 policies namely term plan, endowment plan, child plan, life insurance plan, criteria include income tax benefit, sum assured, benefits on death and riders option. To rank such alternatives, fuzzy vikor expanded form of MCDM (MCDM) technique is used. MCDM ranks policies based on expand S (S) and expand R (R) value. Sensitivity analysis is used to determine the stability in the alternatives ranking, with the varying parameter value V. The proposed approach is compared with fuzzy topsis approach which ranks alternatives based on closeness coefficient value obtained. After ordering the alternatives using the MCDM techniques, it is inferred that, Both the approach almost provides the closest ranking order. The proposed fuzzy vikor provides best and optimal solution preserving the consistency compared to fuzzy topsis.