Recommender Systems (RS) have taken the center place in contemporary e-commerce marketplaces. And RS guides customers towards such items that best meet their preferences and requirements. Although there are many RS techniques including content-based, collaborative filtering, knowledge based etc., each technique has some downsides such as data sparsity, cold-start problem, scalability etc. Therefore, to smoothen these demerits and achieve optimum output from RS, our data scientists have devised the Hybrid Recommender System by composing both content-based and collaborative filtering techniques. And tweaking the algorithms for contextual circumstances such as time, place, occasion, weather etc. Our business is of Fashion and Luxury Outfit domain which is highly influenced by the user's contextual condition and item's constraints. Furthermore, as per previous observations, the items which are purchased or viewed in the similar contexts tend to have similar meanings. Hence, the degree of semantic similarity or relatedness between such items is very high. The recommendation of such items which are contextually and semantically similar, is highly engaging to the customer. Thereby, it increases the probability of the item conversion into sales, views, clicks, bookmark, cart save etc. I will unfold the success story behind the R&D and adaptation of this Hybrid RS.