4. The method according to claim 1 , further comprising labeling a number of users among the group of users using an active learning process thereby dividing the group of users into labeled users and unlabeled users.
4. The method according to claim 1 , further comprising labeling a number of users among the group of users using an active learning process thereby dividing the group of users into labeled users and unlabeled users. 5. The method according to claim 4 , wherein the weighing factor (C) is calculated using the following equation: C = 1 2 ⁢ m ⁢ ( α * ∑ j ∈ Labeled ⁢  f j ⁡ ( x ) - y  2 + β * ∑ j ∈ L ⋃ U ⁢ ∑ i ∈ R ⁢  f ij ⁡ ( x ) - μ j  2 ) wherein ‘m’ represents a total number of users, ‘R’ represents ranking models, L denotes labeled users, U denotes unlabeled users, α represents relative weightage for data of labeled users, β represents relative weightage for consensus between ranking models on the combined data of labeled as well as unlabeled users and wherein for a user ‘j’ among the ‘m’ users, ‘μ j ’ is a mean of the scores provided by each selected model to the ‘j th ’ user.