Abstract: The present disclosure provides a method and system for hybrid recommendation of one or more products through one or more advertisements. The method collects a first set of pre-defined attributes associated with a user of one or more users. In addition, the method classifies the user of the one or more users in one or more clusters based on the first set of pre-defined attributes. Moreover, the method determines one or more characteristics associated with a product of one or more products currently viewed by the user. Further, the method gathers data associated with other one or more products similar to the determined product. Furthermore, the method computes a weighted average propensity to buy for the user. In addition, the method recommends the one or more advertisements associated with the product of the one or more products currently viewed by the user and the other one or more products.

Abstract: The present disclosure provides a method and system for facilitating optimized serving of one or more advertisements on one or more publishers. The advertisement serving optimization system includes a fetching module configured for fetching a first set of parameters associated with one or more advertisers, the one or more publishers and the one or more advertisements associated with the one or more advertisers; a calculation module configured to calculate a probabilistic effective cost per thousand impressions value for a second set of parameters based on the first set of parameters; a priority setting module configured for ranking each of the plurality of advertisement campaigns associated with the corresponding one or more advertisers and an advertisement serving module configured to serve the one or more advertisements to one or more users on the corresponding one or more publishers based on the ranking of each of the plurality of advertisement campaigns.